Publications of Peter Tino
Publications
  of
  Peter Tino




    Journal Papers

  1. B. Li, S.R. Fong, P. Tino: Simple Cycle Reservoirs are Universal.
    Journal of Machine Learning Research, 25(158), pp. 1−28, 2024.
    [JMLR]

  2. L.Y. Lee, D. Vaghari, M.C. Burkhart, P. Tino, M. Montagnese, Zh. Li, K. Zühlsdorff, J. Giorgio, G. Williams, E. Chong, Ch. Chen, B.R. Underwood, T. Rittman, Z. Kourtzi, Alzheimer’s Disease Neuroimaging Initiative: Robust and interpretable AI-guided marker for early dementia prediction in real-world clinical settings.
    eClinicalMedicine, 102725. DOI: https://doi.org/10.1016/j.eclinm.2024.102725, 2024.
    [research data]
    [THE LANCET Discovery Science]

  3. M. A. Raj, P. Awad, R. F. Peletier, R. Smith, U. Kuchner, R. van de Weygaert, N. I. Libeskind, M. Canducci, P. Tino, K. Bunte: The large-scale structure around the Fornax-Eridanus Complex.
    Astronomy and Astrophysics, accepted, 2024.
    [astro-ph preprint server]

  4. M.C. Burkhart, L.Y. Lee, D. Vaghari, An Qi Toh, E. Chong, Ch. Chen, P. Tino, Z. Kourtzi: AI-guided early dementia prediction using unsupervised multimodal modeling of brain health trajectories.
    Scientific Reports, 14, 10755, 2024.
    [supplementary material]
    [Sci Rep]

  5. P. Awad, M. Canducci, E. Balbinot, A. Viswanathan, H.C. Woudenberg, O. Koop, R. Peletier, P. Tino , E. Starkenburg, R. Smith, K. Bunte: Swarming in stellar streams: unveiling the structure of the Jhelum stream with ant colony-inspired computation.
    Astronomy and Astrophysics, 683, A14, 2024.
    [astro-ph preprint server]

  6. M. Yan, C. Huang, P. Bienstman, P. Tino, W. Lin, J. Sun: Emerging opportunities and challenges for the future of reservoir computing.
    Nature Communications, 15, 2056, 2024.
    https://doi.org/10.1038/s41467-024-45187-1
    [Nat Commun]

  7. N. Rodgers, P. Tino, S. Johnson: Strong Connectivity in Real Directed Networks.
    Proceedings of the National Academy of Sciences (PNAS), 120 (12), e2215752120, 2023.
    [arxiv]

  8. F. Tang, P. Tino, H. Yu: Generalized Learning Vector Quantization With Log-Euclidean Metric Learning on Symmetric Positive Definite Manifold.
    IEEE Transactions on Cybernetics, 8(53), pp. 5178-5190, doi: 10.1109/TCYB.2022.3178412, 2023.
    (c) IEEE

  9. N. Rodgers, P. Tino, S. Johnson: Influence and Influenceability: Global Directionality in Directed Complex Networks.
    Royal Society Open Science, 8(10), 221380, 2023.
    [R. Soc. Open Sci.]

  10. K.M. Gokhale, J. Singh Chandan, C. Sainsbury, P. Tino, A. Tahrani, K. Toulis, K. Nirantharakumar: Using Repeated Measurements to Predict Cardiovascular Risk in Patients With Type 2 Diabetes Mellitus.
    The American Journal of Cardiology, 210, pp. 133-142, 2023. Elsevier

  11. B. Ondrusova, P. Tino, J. Svehlikova: A two-step inverse solution for a single dipole cardiac source.
    Frontiers in Physiology, section Visual Neuroscience, 14:1264690. doi: 10.3389/fphys.2023.1264690, 2023.
    [Frontiers in Physiology]

  12. R. Wang, V. Gates, Y. Shen, P. Tino, Z. Kourtzi: Flexible Structure Learning Under Uncertainty.
    Frontiers in Neuroscience, section Cardiac Electrophysiology, accepted, 2023.
    [SSRN]

  13. T. Goodman, K. van Gemst, P. Tino: A Geometric Framework for Pitch Estimation on Acoustic Musical Signals.
    Journal of Mathematics and Music, 17(1), pp. 100-132, 2023. Taylor and Francis

  14. P. Awad, R. Peletier, M. Canducci, R. Smith, A. Taghribi, M. Mohammadi, J. Shin, P. Tino, K. Bunte: Swarm Intelligence-based Extraction and Manifold Crawling Along the Large-Scale Structure.
    Monthly Notices of the Royal Astronomical Society, 3 (520), pp. 4517–4539, 2023. (c) Oxford University Press
    [astro-ph preprint server]

  15. Sh. Zhang, P. Tino, X. Yao: Hierarchical Reduced-Space Drift Detection Framework for Multivariate Supervised Data Streams.
    IEEE Transactions on Knowledge and Data Engineering, 3 (35), pp. 2628-2640, 2023. (c) IEEE

  16. J.S. Altamirano-Flores, L.A. Alvarado-Hernández, J.C. Cuevas-Tello, P. Tino, S.E. Guerra-Palomares, Ch.A. Garcia-Sepulveda: Identification of Clinically Relevant HIV Vif Protein Motif Mutations Through Machine Learning and Undersampling.
    Cells, 5 (12): 772, 2023.
    [MDPI]

  17. J. Giorgio, W. J. Jagust, S. Baker, S. M. Landau, P. Tino, Z. Kourtzi: A robust and interpretable machine learning approach using multimodal biological data to predict future pathological tau accumulation.
    Nature Communications, 13, 1887, 2022.
    doi.org/10.1038/s41467-022-28795-7
    [Nat Commun]

  18. M. Canducci, P. Awad, A. Taghribi, M. Mohammadi, M.Mastropietro, S. De Rijcke, R. Peletier, R. Smith, K. Bunte, P. Tino: 1-DREAM: 1D recovery, extraction and analysis of manifolds in noisy environments.
    Astronomy and Computing, (41), 100658, 2022.
    doi.org/10.1016/j.ascom.2022.100658, ISSN 2213-1337.

  19. N. Rodgers, P. Tino, S. Johnson: Network hierarchy and pattern recovery in directed sparse Hopfield networks.
    Physical Review E, 6 (105), 064304, 2022. (c) American Physical Society

  20. M. Canducci, P. Tino, M. Mastropietro: Probabilistic modelling of general noisy multi-manifold data sets.
    Artificial Intelligence, (302), 103579, 2022. (c) Elsevier

  21. A. Taghribi, K. Bunte, R. Smith, J. Shin, M. Mastropietro, R. F. Peletier, P. Tino: LAAT: Locally Aligned Ant Technique for detecting manifolds of varying density.
    IEEE Transactions on Knowledge and Data Engineering, doi: 10.1109/TKDE.2022.3177368, 2022. (c) IEEE

  22. P. Verzelli, C. Alippi, L. Livi, P. Tino: Input-to-State Representation in linear reservoirs dynamics.
    IEEE Transactions on Neural Networks and Learning Systems, 9 (33), pp. 4598-4609, 2022. (c) IEEE

  23. M. Mohammadi, P. Tino, K. Bunte: Manifold Alignment Aware Ants: a Markovian process for manifold extraction.
    Neural Computation, 34(3), pp. 595-641, 2022. (c) MIT Press

  24. A. Taghribi, M. Canducci, M. Mastropietro, S. De Rijcke, K. Bunte, P. Tino: ASAP - A Sub-sampling Approach for Preserving Topological Structures Modeled with Geodesic Topographic Mapping.
    Neurocomputing, (470), pp. 376-388, 2022. (c) Elsevier

  25. K. Patel, M. Fernandez-Villamarin, C. Ward, J.M. Lord, P. Tino, P.M. Mendes: Establishing a quantitative fluorescence assay for the rapid detection of kynurenine in urine.
    Analyst, (147), pp. 1931-1936, 2022.
    [Analyst]

  26. Y. Zhang, P. Tino, A. Leonardis, K. Tang: A Survey on Neural Network Interpretability.
    IEEE Transactions on Emerging Topics in Computational Intelligence, 5(5), pp. 726-742, 2021. (c) IEEE

  27. F. Tang, H. Feng, P. Tino, B. Si, D. Ji: Probabilistic Learning Vector Quantization on Manifold of Symmetric Positive Definite Matrices.
    Neural Networks, 142, pp. 105-118, 2021. (c) Elsevier
    [arXiv]

  28. R. Pauli et al.: Machine Learning Classification of Conduct Disorder with High Versus Low Levels of Callous-Unemotional Traits Based on Facial Emotion Recognition Abilities.
    European Child & Adolescent Psychiatry, doi: 10.1007/s00787-021-01893-5 (Epub ahead of print), PMID: 34661765, 2021. (c) Springer

  29. F. Tang, M. Fan, P. Tino: Generalized Learning Riemannian Space Quantization: a Case Study on Riemannian Manifold of SPD Matrices.
    IEEE Transactions on Neural Networks and Learning Systems, 1 (32), pp. 281-292, 2021. (c) IEEE

  30. S.K. Cavdar, T.T. Temizel, A. Mehrotra, M. Musolesi, P. Tino: Designing Robust Models for Behaviour Prediction using Sparse Data from Mobile Sensing: A Case Study of Office Workers' Availability for Wellbeing Interventions.
    ACM Transactions on Computing for Healthcare, 4(2), pp. 29:1-29:33, 2021. (c) ACM

  31. P. Tino: Dynamical Systems as Temporal Feature Spaces.
    Journal of Machine Learning Research, 21(44), pp. 1−42, 2020.
    [JMLR]

  32. I. Akerman et al.: A predictable conserved DNA base composition signature defines human core DNA replication origins.
    Nature Communications, 11, 4826, 2020.
    doi.org/10.1038/s41467-020-18527-0
    [Nat Commun]

  33. J. Giorgio, S. Landau, W. Jagust, P. Tino, Z. Kourtzi: Modelling prognostic trajectories of cognitive decline due to Alzheimer’s disease.
    NeuroImage: Clinical, 26, 102199, 2020.
    doi: https://doi.org/10.1016/j.nicl.2020.102199. Elsevier

  34. L. Pfannschmidt, J. Jakob, F. Hinder, M. Biehl, P. Tino, B. Hammer: Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information.
    Neurocomputing, (416), pp. 266-279, 2020. (c) Elsevier

  35. S.K. Cavdar, T. Taskaya-Temizel, M. Musolesi, P. Tino: A Multi-perspective Analysis of Social Context and Personal Factors in Office Settings for the Design of an Effective Mobile Notification System.
    Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 1 (4), Article No. 15, pp. 1-38, 2020. Association for Computing Machinery

  36. F-M. Schleif, Ch. Raab, P. Tino: Sparsification of Core Set Models in Non-metric Supervised Learning.
    Pattern Recognition Letters, 129, pp. 1-7, 2020. (c) Elsevier

  37. K. M. Gokhale, J. S. Chandan, K. Toulis, G. Gkoutos, P. Tino, K. Nirantharakumar: Data extraction for epidemiological research (DExtER): a novel tool for automated clinical epidemiology studies.
    European Journal of Epidemiology, https://doi.org/10.1007/s10654-020-00677-6, 2020.
    [pdf]

  38. R. Pauli et al.: Positive and Negative Parenting in Conduct Disorder with High versus Low Levels of Callous-Unemotional Traits.
    Development and Psychopathology, 1-12. doi:10.1017/S0954579420000279, 2020. Cambridge University Press

  39. S.Y. Chong, P. Tino, J. He: Coevolutionary Systems and PageRank.
    Artificial Intelligence, (277), pp. 103164, 2019. (c) Elsevier

  40. V. Karlaftis, J. Giorgio, P. Vertes, R. Wang, Y. Shen, P. Tino, A. Welchman, Z. Kourtzi: Multimodal imaging of brain connectivity reveals predictors of individual decision strategy in statistical learning.
    Nature Human Behaviour, 3, pp. 297–307, 2019. (c) Springer Nature
    [pdf] [Supplementary Information]

  41. J. Powell, S. Stevenson, I. Mandel, P. Tino: Unmodelled Clustering Methods for Gravitational Wave Populations of Compact Binary Mergers.
    Monthly Notices of the Royal Astronomical Society, 3 (488), pp. 3810-3817, 2019. (c) Oxford University Press
    [astro-ph preprint server]

  42. G. Longo, E. Merenyi, P. Tino: Foreword to the Focus Issue on Machine Intelligence in Astronomy and Astrophysics.
    Publications of the Astronomical Society of the Pacific, 1004 (131), pp. 100101, 2019. IOP Publishing.
    [astro-ph preprint server]

  43. A. Elhabbash, M. Salama, R. Bahsoon, P. Tino: Self-Awareness in Software Engineering: A Systematic Literature Review.
    ACM Transactions on Autonomous and Adaptive Systems, 2(14), 5, 2019. ACM DL

  44. S. Y. Chong, P. Tino, J. He, X. Yao: A New Framework for Analysis of Coevolutionary Systems - Directed Graph Representation and Random Walks.
    Evolutionary Computation, 2(27), pp. 195-228, 2019. (c) MIT Press

  45. K. Bunte, D.J. Smith, M.J. Chappell, Z.K. Hassan-Smith, J.W. Tomlinson, W. Arlt, P. Tino: Learning Pharmacokinetic Models for in vivo Glucocorticoid Activation.
    Journal of Theoretical Biology, 455, pp. 222-231, DOI:10.1016/j.jtbi.2018.07.025, 2018. Elsevier Open Access

  46. V.M. Karlaftis,R. Wang, Y. Shen, P. Tino, G. Williams, A.E. Welchman, Z. Kourtzi: White-Matter Pathways for Statistical Learning of Temporal Structures.
    eNeuro, 0382-17.2018, doi.org/10.1523/ENEURO.0382-17.2018, 2018.
    [pdf]

  47. F. Schleif, A. Gisbrecht, P. Tino: Supervised Low Rank Indefinite Kernel Approximation Using Minimum Enclosing Balls.
    Neurocomputing, 318, pp. 213-226, doi.org/10.1016/j.neucom.2018.08.057, 2018. (c) Elsevier

  48. P. Tino: Asymptotic Fisher Memory of Randomized Linear Symmetric Echo State Networks.
    Neurocomputing, 298, pp. 4–8, 2018. (c) Elsevier

  49. M. Rupawala, H. Dehghani, S. J. E. Lucas, P. Tino, D Cruse: Shining a Light on Awareness: A Review of Functional Near-Infrared Spectroscopy for Prolonged Disorders of Consciousness.
    Frontiers in Neurology, 9, pp. 350-367, DOI: 1doi.org/10.3389/fneur.2018.00350, 2018.
    [pdf]

  50. J. Giorgioa, V. M. Karlaftis, R. Wang,Y. Shen, P. Tino, A. Welchman, Z. Kourtzi: Functional brain networks for learning predictive statistics.
    Cortex, 107, pp. 204-219, DOI: 10.1016/j.cortex.2017.08.014, 2018. (c) Elsevier

  51. Z. Kourtzi, R. Wang,Y. Shen, P. Tino, A. Welchman: Learning predictive statistics: strategies and brain mechanisms.
    The Journal of Neuroscience, 37(35), pp. 8412–8427, 2017. (c) Society for Neuroscience

  52. Y. Shen, P. Tino, K. Tsaneva-Atanasova: Classification framework for partially observed dynamical systems.
    Physical Review E, 95, 043303, 2017. (c) American Physical Society

  53. R. Wang,Y. Shen, P. Tino, A. Welchman, Z. Kourtzi : Learning predictive statistics from temporal sequences: dynamics and strategies.
    Journal of Vision, 17(12):1, pp. 1–16, doi:10.1167/17.12.1, 2017. (c) IOVS
    [Supplementary Information]

  54. F.M. Schleif, P. Tino: Indefinite Core Vector Machine.
    Pattern Recognition, 71, pp.187-195, 2017. (c) Elsevier

  55. F. Tang, P. Tino: Ordinal Regression based on Learning Vector Quantization.
    Neural Networks, (93), pp. 76-88, 2017. (c) Elsevier

  56. F. Tsapeli, M. Musolesi, P. Tino: Non-parametric Causality Detection: An Application to Social Media and Financial Data.
    Physica A: Statistical Mechanics and its Applications, (483), pp. 139-155, 2017. (c) Elsevier

  57. H.K. Wong, P.A. Tiffin, M.J. Chappell, T.E. Nichols, P.R. Welsh, O. Doyle, B.C. Lopez-Kolkovska, S.K. Inglis, D. Coghill, Y. Shen, P. Tino: Personalized Medication Response Prediction for Attention-Deficit Hyperactivity Disorder: Learning in the Model Space Versus Learning in the Data Space.
    Frontiers in Physiology, section Computational Physiology and Medicine, 8:199. doi: 10.3389/fphys.2017.00199, 2017.
    [pdf]

  58. P. Slowinski, F. Alderisio, C. Zhai, Y. Shen, P. Tino, C. Bortolon, D. Capdevielle, L. Cohen, M. Khoramshahi, A. Billard, R. Salesse, M. Gueugnon, L. Marin, B.G. Bardy, M. di Bernardo, S. Raffard, K. Tsaneva-Atanasova: Unravelling socio-motor biomarkers in schizophrenia.
    npj Schizophrenia, 1(3), DOI:10.1038/s41537-016-0009-x, 2017.

  59. I. Mandel, W.M. Farr, A. Colonna, S. Stevenson, P. Tino, J. Veitch: Model-independent inference on compact-binary observations.
    Monthly Notices of the Royal Astronomical Society, 3 (465), pp. 3254-3260, 2017. (c) Oxford University Press
    [astro-ph preprint server]

  60. H. Alahmadi, Y. Shen, Sh. Fouad, C. Luft, P. Bentham, Z. Kourtzi, P. Tino: Classifying cognitive profiles using machine learning with privileged information in Mild Cognitive Impairment.
    Frontiers in Computational Neuroscience, (10), pp. 117, 2016.
    [pdf]

  61. S. AL Otaibi, P. Tino, J. C. Cuevas-Tello, I. Mandel, S. Raychaudhury: Kernel regression estimates of time delays between gravitationally lensed fluxes.
    Monthly Notices of the Royal Astronomical Society, 1(459), pp. 139–146, 2016. (c) Oxford University Press
    [astro-ph preprint server]

  62. N. Gianniotis, S.D. Kuegler, P. Tino, K.L. Polsterer: Model-Coupled Autoencoder for Time Series Visualisation.
    Neurocomputing, (192), pp. 139–146, 2016. (c) Elsevier
    [astro-ph preprint server]

  63. M. Perez-Ortiz, P. A. Gutierrez, P. Tino, C. Hervas-Martinez: Over-sampling the minority class in the feature space.
    IEEE Transactions on Neural Networks and Learning Systems, 9(27), pp. 1947-1961, 2016. (c) IEEE

  64. F. Schleif, P. Tino: Indefinite proximity learning - A review.
    Neural Computation, 10(27), pp. 2039-2096, 2015. (c) MIT Press

  65. F. Tang, P. Tino, P. A. Gutierrez, H. Chen: The Benefits of Modelling Slack Variables in SVMs.
    Neural Computation, 4(27), pp. 954-981, 2015. (c) MIT Press

  66. P. A. Gutierrez, P. Tino, C. Hervas-Martınez: Ordinal regression neural networks based on concentric hyperspheres.
    Neural Networks, 59, pp. 51-60, 2014. (c) Elsevier

  67. Y. Shen, S.D. Mayhew, Z. Kourtzi, P. Tino: Spatial-temporal modelling of fMRI data through spatially regularized mixture of hidden process models.
    Neuroimage, 84(1), pp. 657-671, 2014. (c) Elsevier

  68. H. Chen, P. Tino, X. Yao, A. Rodan: Learning in the Model Space for Fault Diagnosis.
    IEEE Transactions on Neural Networks and Learning Systems, 25(1), pp. 124-136, 2014. (c) IEEE

  69. H. Chen, P. Tino, X. Yao: Efficient Probabilistic Classification Vector Machine with Incremental Basis Function Selection.
    IEEE Transactions on Neural Networks and Learning Systems, 25(2), pp. 356-369, 2014. (c) IEEE

  70. H. Chen, P. Tino, X. Yao: Cognitive Fault Diagnosis in Tennessee Eastman Process using Learning in the Model Space.
    Computers & Chemical Engineering, 67, pp. 33-42, 2014. (c) Elsevier

  71. J. Mazgut, P. Tino, M. Boden, H. Yang: Dimensionality Reduction and Topographic Mapping of Binary Tensors.
    Pattern Analysis and Applications, 17(3), pp. 497-515, 2014. (c) Springer

  72. J. Quevedo, H. Chen, M. A. Cuguero, P. Tino, V. Puig, D. Garcia, R. Sarrate, X. Yao: Combining Learning in Model Space Fault Diagnosis with Data Validation/Reconstruction: Application to the Barcelona Water Network.
    Engineering Applications of Artificial Intelligence, (30), pp. 18-29, 2014. (c) Elsevier

  73. Ph. Weber, B. Bordbar, P. Tino: A Framework for the Analysis of Process Mining Algorithms.
    IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans, 41(2), pp. 303-317, 2013. (c) IEEE

  74. P. Tino, S.Y. Chong, X. Yao: Complex Co-Evolutionary Dynamics - Structural Stability and Finite Population Effects.
    IEEE Transactions on Evolutionary Computation, 17(2), pp. 155-164, 2013. (c) IEEE

  75. P. Tino: Pushing for the Extreme: Estimation of Poisson Distribution from Low Count Unreplicated Data - How Close Can We Get?
    Entropy, 15(4), pp. 1202-1220, 2013.
    Special Issue on Distance in Information and Statistical Physics.

  76. S. Fouad, P. Tino, S. Raychaudhury, P. Schneider: Incorporating Privileged Information Through Metric Learning.
    IEEE Transactions on Neural Networks and Learning Systems, 24(7), pp. 1086 - 1098, 2013. (c) IEEE

  77. J. Sanchez-Monedero, P. A. Gutierrez, P. Tino, C. Hervas- Martınez: Exploitation of Pairwise Class Distances for Ordinal Classification.
    Neural Computation, 25(9), pp. 2450-2485, 2013. (c) MIT Press
    code + data

  78. P. Tino, A. Rodan: Short Term Memory in Input-Driven Linear Dynamical Systems.
    Neurocomputing, 112, pp. 58-63, 2013. (c) Elsevier

  79. W. Dong, T. Chen, P. Tino, X. Yao: Scaling Up Estimation of Distribution Algorithms for Continuous Optimization.
    IEEE Transactions on Evolutionary Computation, 17(6), pp. 797 - 822, 2013. (c) IEEE

  80. N. Nikolaev, P. Tino, E. Smirnov: Time-dependent series variance learning with recurrent mixture density networks .
    Neurocomputing, 122, pp. 501–512, 2013. (c) Elsevier

  81. O. M. Doyle, K. Tsaneva-Atansaova, J. Harte, P. A.Tiffin, P. Tino, V. Diaz-Zuccarini: Bridging Paradigms: Hybrid Mechanistic-Discriminative Predictive Models.
    IEEE Transactions on Biomedical Engineering , 60(3), pp. 735-742, 2013. (c) IEEE

  82. Sh. Fouad, P. Tino: Adaptive Metric Learning Vector Quantization for Ordinal Classification.
    Neural Computation, 24(11), pp. 2825-2851, 2012. (c) MIT Press

  83. A. Rodan, P. Tino: Simple Deterministically Constructed Cycle Reservoirs with Regular Jumps.
    Neural Computation, 24(7), pp. 1822-1852, 2012. (c) MIT Press

  84. B. Rudolf, M. Markosova, M. Cajagy, P. Tino: Degree Distribution and Scaling in the Connecting - Nearest - Neighbors Model.
    Physical Review E, 85(2), 026114, 2012. (c) American Physical Society

  85. S.Y. Chong, P. Tino, D. C. Ku, X. Yao: Improving Generalization Performance in Co-evolutionary Learning.
    IEEE Transactions on Evolutionary Computation, 16(1), pp 70-85, 2012. (c) IEEE

  86. A. Rodan, P. Tino: Minimum Complexity Echo State Network.
    IEEE Transactions on Neural Networks, 22(1), pp 131-144, 2011. (c) IEEE

  87. S. T. McClain, P. Tino, R. E. Kreeger: Ice Shape Characterization Using Self-Organizing Maps.
    Journal of Aircraft, 48(2), pp 724-729, 2011. (c) American Institute of Aeronautics and Astronautics

  88. P. Tino, Z. Hongya, H. Yan: Searching for co-expressed genes in three-color cDNA microarray data using a probabilistic model based Hough Transform.
    IEEE/ACM Transactions on Computational Biology and Bioinformatics, 8(4), pp 1093-1107, 2011. (c) IEEE

  89. J. Binner, P. Tino, J. Tepper, R. Anderson, B. Jones, G. Kendall: Does Money Matter in Inflation Forecasting?.
    Physica A: Statistical Mechanics and its Applications, 389(21), pp 4793-4808, 2010. (c) Elsevier

  90. J. C. Cuevas-Tello, P. Tino, S. Raychaudhury, X. Yao, M. Harva: Uncovering delayed patterns in noisy and irregularly sampled time series: An astronomy application.
    Pattern Recognition, 43(3), pp 1165-1179, 2010. (c) Elsevier   [astro-ph preprint server]

  91. S.Y. Chong, P. Tino, X. Yao: Relationship between generalization and diversity in coevolutionary learning.
    IEEE Transactions on Computational Intelligence and AI in Games, 1(3), pp 214-232, 2009. (c) IEEE

  92. P. Tino: Basic Properties and Information Theory of Audic-Claverie Statistic for Analyzing cDNA Arrays.
    BMC Bioinformatics, 10:310, 2009.

  93. H. Chen, P. Tino, X. Yao: Probabilistic Classification Vector Machines.
    IEEE Transactions on Neural Networks, 20(6), pp 901-914, 2009. (c) IEEE
    IEEE Transactions on Neural Networks Outstanding 2009 Paper Award (IEEE Computational Intelligence Society, 2011)

  94. P. Tino: Bifurcation Structure of Equilibria of Iterated Softmax.
    Chaos, Solitons & Fractals, 41, pp 1804-1816, 2009. (c) Elsevier

  95. H. Chen, P. Tino, X. Yao: Predictive Ensemble Pruning by Expectation Propagation.
    IEEE Transactions on Knowledge and Data Engineering, 21(7), pp 999-1013, 2009. (c) IEEE

  96. N. Gianniotis, P. Tino: Visualisation of Tree-Structured Data through Generative Topographic Mapping.
    IEEE Transactions on Neural Networks, 19(8), pp 1468-1493, 2008. (c) IEEE

  97. S.Y. Chong, P. Tino, X. Yao: Measuring Generalization Performance in Co-evolutionary Learning.
    IEEE Transactions on Evolutionary Computation, 12(4), pp 479-505, 2008. (c) IEEE
    IEEE Transactions on Evolutionary Computation Outstanding 2008 Paper Award (IEEE Computational Intelligence Society, 2010)

  98. P. Tino: Equilibria of Iterative Softmax and Critical Temperatures for Intermittent Search in Self-Organizing Neural Networks.
    Neural Computation, 19(4), pp. 1056-1081, 2007. (c) MIT Press

  99. P. Tino, I. Farkas, J.van Mourik: Dynamics and Topographic Organization of Recursive Self-Organizing Maps.
    Neural Computation, 18(10), pp. 2529-2567, 2006. (c) MIT Press

  100. J.C. Cuevas-Tello, P. Tino, S. Raychaudhury: How accurate are the time delay estimates in gravitational lensing?
    Astronomy and Astrophysics, 454(3), pp 695-706, 2006. (c) Springer-Verlag
    [astro-ph preprint server]

  101. P. Tino, A. Mills: Learning Beyond Finite Memory in Recurrent Networks Of Spiking Neurons.
    Neural Computation, 18(3), pp. 561-613, 2006. (c) MIT Press

  102. G. Brown, J. Wyatt, P. Tino: Managing Diversity in Regression Ensembles.
    Journal of Machine Learning Research, 6, pp. 1621-1650, 2005.

  103. I. Nabney, Y. Sun, P. Tino, A. Kaban: Semisupervised Learning of Hierarchical Latent Trait Models for Data Visualization.
    IEEE Transactions on Knowledge and Data Engineering, 17(3), pp. 384-400, 2005. (c) IEEE

  104. G. Polcicova, P. Tino: Making sense of sparse rating data in collaborative filtering via topographic organization of user preference patterns.
    Neural Networks, 17(8-9), pp.1183-1199, 2004. (c) Elsevier

  105. P. Tino, I. Nabney, B.S. Williams, J. Losel, Y. Sun: Non-linear Prediction of Quantitative Structure-Activity Relationships.
    Journal of Chemical Information and Computer Sciences, 44(5), pp. 1647-1653, 2004. (c) ACM

  106. P. Tino, M. Cernansky, L. Benuskova: Markovian architectural bias of recurrent neural networks.
    IEEE Transactions on Neural Networks, 15(1), pp. 6-15, 2004. (c) IEEE

  107. B. Hammer, P. Tino: Recurrent neural networks with small weights implement definite memory machines.
    Neural Computation, 15(8), pp. 1897-1926, 2003. (c) MIT Press

  108. P. Tino, B. Hammer: Architectural Bias in Recurrent Neural Networks: Fractal Analysis.
    Neural Computation, 15(8), pp. 1931-1957, 2003. (c) MIT Press

  109. P. Tino, G. Polcicova: Topographic organization of user preference patterns in collaborative filtering.
    Neural Network World, 13(3), pp. 311-324, 2003. (c) CAS

  110. Ch. Schittenkopf, P. Tino, G. Dorffner: The Benefit of Information Reduction for Trading Strategies.
    Applied Financial Economics, 34(7), pp. 917-930, 2002. (c) Routledge

  111. P. Tino: Multifractal properties of Hao's geometric representations of DNA sequences.
    Physica A: Statistical Mechanics and its Applications, 304(3-4), pp. 480-494, 2002. (c) Elsevier

  112. P. Tino, I. Nabney: Hierarchical GTM: constructing localized non-linear projection manifolds in a principled way.
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(5), pp. 639-656, 2002. (c) IEEE

  113. P. Tino, Ch. Schittenkopf, G. Dorffner: Financial Volatility Trading using Recurrent Neural Networks.
    IEEE Transactions on Neural Networks, 12(4), pp. 865-874, 2001. (c) IEEE

  114. P. Tino, Ch. Schittenkopf, G. Dorffner: Volatility Trading via Temporal Pattern Recognition in Quantized Financial Time Series.
    Pattern Analysis and Applications, 4(4), pp. 283-299, 2001. (c) Springer-Verlag

  115. P. Tino, B.G. Horne, C.L. Giles: Attractive Periodic Sets in Discrete Time Recurrent Networks (with Emphasis on Fixed Point Stability and Bifurcations in Two--Neuron Networks).
    Neural Computation, 13(6), pp. 1379-1414, 2001. (c) MIT Press

  116. P. Tino, G. Dorffner: Predicting the future of discrete sequences from fractal representations of the past.
    Machine Learning, 45(2), pp. 187-218, 2001. (c) Kluwer

  117. P. Tino, M. Koteles: Extracting finite state representations from recurrent neural networks trained on chaotic symbolic sequences.
    IEEE Transactions on Neural Networks, 10(2), pp. 284-302, 1999. (c) IEEE

  118. P. Tino: Spatial Representation of Symbolic Sequences through Iterative Function Systems.
    IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans, 29(4), pp. 386-392, 1999.
    Click here for figures . (c) IEEE

  119. P. Tino, V. Vojtek: Extracting finite state representations from recurrent neural networks.
    Neural Network World, 8(5), pp. 517-530, 1998. (c) CAS

  120. P. Petrovic, P. Tino, L. Benuskova: Processing symbolic sequences by the BCM neuron.
    Neural Network World, 8(5), pp. 491-500, 1998. (c) CAS

  121. T. Lin, B.G. Horne, P. Tino, C.L. Giles: Learning long-term dependencies with NARX recurrent neural networks.
    IEEE Transactions on Neural Networks, 7(6), pp. 1329-1338, 1996. (c) IEEE
    IEEE Transactions on Neural Networks Outstanding 1996 Paper Award (IEEE, 1998)

  122. P. Tino, J. Sajda: Learning and Extracting Initial Mealy Machines with a Modular Neural Network Model.
    Neural Computation, 7(4), pp. 822-844, 1995. (c) MIT Press

  123. P. Tino, J. Sajda: Modifications of a Self-Referencing System.
    Computers and Artificial Intelligence, 12(2), pp. 131-144, 1993.


    Books - Monographs

  124. R.S. Fong, P. Tino: Population-Based Optimization on Riemannian Manifolds.
    Springer, Studies in Computational Intelligence (SCI), volume 1046, 2022.
    ISBN 978-3-031-04292-8 (eBook ISBN 978-3-031-04293-5)

  125. V. Kvasnicka, J. Pospichal, P. Tino: Evolutionary algorithms (in Slovak).
    STU, Bratislava, ISBN 8022713775, 2000.

  126. V. Kvasnicka, L. Benuskova, J. Pospichal, I. Farkas, P. Tino, A. Kral: Introduction to the Theory of Neural Networks (in Slovak).
    IRIS, Bratislava, 1997.


    Edited Books

  127. H. Yin, D. Camacho, P. Tino, R. Allmendinger, A.J. Tallon-Ballesteros, K. Tang, S-B Cho, P. Novais, S. Nascimento (Eds.): Intelligent Data Engineering and Automated Learning - IDEAL 2021.
    Springer, LNCS vol. 13113, ISBN 978-3-030-91607-7, 2021.

  128. H. Yin, D. Camacho, P. Tino, A.J. Tallon-Ballesteros, R. Menezes, R. Allmendinger (Eds.): Intelligent Data Engineering and Automated Learning - IDEAL 2019.
    Springer, ISBN 978-3-030-33606-6, 2019.

  129. C. Fyfe, P Tino, D. Charles, C. Garcia-Osorio, H. Yin (Eds.): Intelligent Data Engineering and Automated Learning - IDEAL 2010.
    Springer, Lecture Notes in Computer Science, Vol. 6283, 2010.

  130. H. Yin, P Tino, E. Corchado, W. Byrne, X. Yao (Eds.): Intelligent Data Engineering and Automated Learning - IDEAL 2007.
    Springer, Lecture Notes in Computer Science, Vol. 4881, 2007.

  131. X. Yao, E. Burke, J.A. Lozano, J. Smith,J.J. Merelo-Guervos, J.A. Bullinaria, J. Rowe, P. Tino, A. Kaban, H.P. Schwefel (Eds.): Parallel Problem Solving from Nature - PPSN VIII .
    Springer, Lecture Notes in Computer Science, Vol. 3242, 2004.


    Book Chapters and Refereed Conference Papers

  132. P. Tino, R.S. Fong, R.F. Leonarduzzi: Predictive Modeling in the Reservoir Kernel Motif Space.
    In International Joint Conference on Neural Networks - IJCNN 2024, 2024.

  133. G. Serra, P. Tino, Z. Xu, X. Yao: An Interpretable Alternative to Neural Representation Learning for Rating Prediction - Transparent Latent Class Modeling of User Reviews.
    In International Joint Conference on Neural Networks - IJCNN 2024, 2024.

  134. S. Friess, P. Tino, S. Menzel, Z. Xu, B. Sendhoff, X. Yao: Spatio-Temporal Activity Recognition for Evolutionary Search Behavior Prediction.
    In International Joint Conference on Neural Networks - IJCNN 2022, doi: 10.1109/IJCNN55064.2022.9892483, 2022.

  135. Sh. Zhang, Ch. Pan, L. Song, X. Wu, Z. Hu, K. Pei, P. Tino, X. Yao: Label-Assisted Memory Autoencoder for Unsupervised Out-of-Distribution Detection.
    In European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - ECML/PKDD, Proceedings Part III, pp. 795–810, 2021.

  136. A. Elhabbash, R. Bahsoon, P. Tino, P. Lewis, Y. Elkhatib: Attaining Meta-self-awareness through Assessment of Quality-of-Knowledge.
    In IEEE International Conference on Web Services - ICWC 2021, pp. 712-723, 2021.
    doi: 10.1109/ICWS53863.2021.00099

  137. M. Canducci, A. Taghribi, M. Mastropietro, S. de Rijcke, R. Peletier, K. Bunte, P. Tino: Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations.
    22nd International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2021), pp. 493--501, Lecture Notes in Computer Science, Springer International Publishing, LNCS 13113, 2021.

  138. X. Chen, Y. Shen, E. Zavala, K. Tsaneva-Atanasova, T. Upton, G. Russell, P. Tino: SOMiMS - Topographic Mapping in the Model Space.
    22nd International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2021), pp. 502-510, Lecture Notes in Computer Science, Springer International Publishing, LNCS 13113, 2021.

  139. S. Friess, P. Tino, Z. Xu, S. Menzel, B. Sendhoff, X. Yao: Artificial Neural Networks as Feature Extractors in Continuous Evolutionary Optimization.
    In International Joint Conference on Neural Networks - IJCNN 2021, doi: 10.1109/IJCNN52387.2021.9533915, 2021.

  140. G. Serra, Z. Xu, M. Niepert, C. Lawrence, P. Tino, X. Yao: Interpreting Node Embedding with Text-labeled Graphs.
    In International Joint Conference on Neural Networks - IJCNN 2021, doi: 10.1109/IJCNN52387.2021.9533692, 2021.

  141. S. Friess, P. Tino, S. Menzel, B. Sendhoff, X. Yao: Improving Sampling in Evolution Strategies Through Mixture-Based Distributions Built from Past Problem Instances.
    In Parallel Problem Solving from Nature – PPSN XVI, Lecture Notes in Computer Science, vol 12269, pp 583–596. Springer, 2020.

  142. S. Ghosh, P. Tino, K. Bunte: Visualization and knowledge discovery from interpretable models.
    In International Joint Conference on Neural Networks - IJCNN 2020, doi.org/10.1109/IJCNN48605.2020.9206702, IEEE Computer Society, 2020.

  143. A. Taghribi, K. Bunte, M. Mastropietro, S. D. Rijcke, P. Tino: ASAP - A Sub-sampling Approach for Preserving Topological Structures.
    European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2020). D-facto Publications, ISBN 978-2-87587-074-2, 2020.

  144. M. Perez-Ortiz, P. Tino, R. Mantiuk, S. C. Hervás Martínez: Exploiting Synthetically Generated Data with Semi-Supervised Learning for Small and Imbalanced Datasets.
    In AAAI Conference on Artificial Intelligence - AAAI 2019, doi 10.17863/CAM.36472, AAAI, 2019.

  145. M. Perez-Ortiz, P. A. Gutierrez, P. Tino, C. Casanova-Mateo, S. Salcedo-Sanz: A mixture of experts model for predicting persistent weather patterns.
    In International Joint Conference on Neural Networks - IJCNN 2018, doi 10.1109/IJCNN.2018.8489179, IEEE Computer Society, 2018.

  146. P. Tino: Fisher Memory of Linear Wigner Echo State Networks.
    European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2017). pp. 87-92, D-facto Publications, 2017.

  147. Y. Shen, P. Tino, K. Tsaneva-Atanasova: Classification of sparsely and irregularly sampled time series: A learning in model space approach.
    In International Joint Conference on Neural Networks - IJCNN 2017, pp. 3696-3703, IEEE Computer Society, 2017.

  148. L. Pasa, A. Sperduti, P. Tino: Linear dynamical based models for sequential domains.
    In International Joint Conference on Neural Networks - IJCNN 2017, pp. 2201-2208, IEEE Computer Society, 2017.

  149. F. Tsapeli, P. Tino, M. Musolesi: Probabilistic matching: Causal inference under measurement errors.
    In International Joint Conference on Neural Networks - IJCNN 2017, pp. 278-285, IEEE Computer Society, 2017.

  150. A. Elhabbash, R. Bahsoon, P. Tino: Self-Awareness for Dynamic Knowledge Management in Self-Adaptive Volunteer Services.
    IEEE International Conference on Web Services (ICWS). pp. 180-187, IEEE Computer Society, 2017.

  151. S. Ghosh, E.S. Baranowski, R. van Veen, G-J de Vries, M. Biehl, W. Arlt, P. Tino, K. Bunte: Comparison of strategies to learn from imbalanced classes for computer aided diagnosis of inborn steroidogenic disorders.
    European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2017). pp. 177-186, D-facto Publications, 2017.

  152. A. Elhabbash, R. Bahsoon, P. Tino: Interaction-Awareness for Self-Adaptive Volunteer Computing.
    10th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO). pp. 148--149, IEEE Computer Society, 2016.

  153. N. Alowadi, Y. Shen, P. Tino: Prototype-Based Spatio-Temporal Probabilistic Modelling of fMRI Data.
    Advances in Self-Organizing Maps and Learning Vector Quantization, pp. 193-203, Springer-Verlag, Advances in Intelligent Systems and Computing 428, 2016.

  154. S. Al Otaibi, P. Tino, S. Raychaudhury: Probabilistic Modelling for Delay Estimation in Gravitationally Lensed Photon Streams.
    17th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2016), pp. 552-559, Lecture Notes in Computer Science, Springer-Verlag, LNCS 9937, 2016.
    Best Conference Paper Award (Applications track)

  155. F.M. Schleif, P. Tino, Y. Liang: Learning in indefinite proximity spaces - recent trends.
    European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2016). pp. l13-l22, 2016.

  156. F.M. Schleif, A. Kaban, P. Tino: Finding Small Sets of Random Fourier Features for Shift-Invariant Kernel Approximation.
    In Artificial Neural Networks in Pattern Recognition - IAPR Workshop on Artificial Neural Networks in Pattern Recognition, pp. 323-331, Lecture Notes in Computer Science 9896, Springer-Verlag, 2016.

  157. H. Chen, F. Tang, P. Tino, A. G. Cohn, X. Yao: Model Metric Co-learning for Time Series Classification.
    In 28th International Joint Conference on Artificial Intelligence (IJCAI 2015), pp. 3387-3394, AAAI Press, 2015.

  158. R.T. Ibrahem, P. Tino, R.J. Pearson, T.J. Ponman, A. Babul: Automated Detection of Galaxy Groups Through Probabilistic Hough Transform.
    Neural Information Processing - Proceedings of the 22nd International Conference on Neural Information Processing (ICONIP 2015). pp. 323-331, Lecture Notes in Computer Science 9491, Springer-Verlag, 2015.

  159. A. Elhabbash, R. Bahsoon, P. Tino, P.R. Lewis: Self-adaptive Volunteered Services Composition through Stimulus- and Time-awareness.
    Proceedings of the 22nd IEEE International Conference on Web Services (ICWS 2015). pp. 57-64, IEEE Computer Society, 2015.

  160. N. Gianniotis, S.D. Kuegler, P. Tino, K. Polsterer, R. Misra: Autoencoding time series for visualisation.
    European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2015). pp. 495-500, 2015.

  161. F.M. Schleif, A. Gisbrecht, P. Tino: Probabilistic Classification Vector Machine at large scale.
    European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2015). pp. 555-560, 2015.

  162. F. Schleif, H. Chen, P. Tino: Incremental Probabilistic Classification Vector Machine with linear costs.
    In International Joint Conference on Neural Networks - IJCNN 2015, pp.1-8, IEEE Computer Society, 2015.

  163. F.M. Schleif, A. Gisbrecht, P. Tino: Large scale Indefinite Kernel Fisher Discriminant.
    3rd International Workshop on Similarity-Based Pattern Analysis and Recognition (SIMBAD 2015). pp. 160-170, Lecture Notes in Computer Science 9370, Springer-Verlag, 2015.

  164. A. Elhabbash, R. Bahsoon, P. Tino, P.R. Lewis: A Utility Model for Volunteered Service Composition.
    Proceedings of the 7th IEEE/ACM International Conference on Utility and Cloud Computing (UCC 2014). pp. 337-344, IEEE Computer Society, 2014.

  165. F. Tang, P. Tino, H. Chen: Learning the Deterministically Constructed Echo State Networks.
    In International Joint Conference on Neural Networks - IJCNN 2014, pp. 77-83, IEEE Computer Society, 2014.

  166. F. Tang, P. Tino, P. A. Gutierrez Pena, H. Chen: Support Vector Ordinal Regression using Privileged Information.
    European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2014). i6doc.com publ., 2014.

  167. A. Elhabbash, R. Bahsoon, P. Tino: Towards Self-aware Service Composition.
    16th IEEE International Conference on High Performance Computing and Communications. pp. 1275-1279, IEEE Press, 2014.

  168. Y. Shen, S.D. Mayhew, Z. Kourtzi, P. Tino: A spatial mixture approach to inferring sub-ROI spatio-temporal patterns from rapid event-related fMRI data.
    16th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2013), pp. 657-664, Lecture Notes in Computer Science, Springer-Verlag, LNCS 8150, 2013.

  169. H. Chen, F. Tang, P. Tino, X. Yao: Model-based Kernel for Efficient Time Series Analysis.
    19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'13), pp. 392-400, ACM New York, NY, USA. 2013.

  170. S. Fouad, P. Tino: Ordinal-Based Metric Learning for Learning Using Privileged Information.
    In International Joint Conference on Neural Networks - IJCNN 2013, accepted IEEE Computer Society, 2013.

  171. P. Weber, B. Bordbar, P. Tino: A Principled Approach to Mining From Noisy Logs Using Heuristics Miner .
    IEEE Symposium on Computational Intelligence and Data Mining (CIDM 2013), pp. 119-126, 2013.

  172. P. Tino, S. Raychaudhury: Computational Intelligence in Astronomy – A Win-Win Situation.
    In Theory and Practice of Natural Computing, pp. 57-71, Lecture Notes in Computer Science, Springer-Verlag, LNCS 7505, 2012.

  173. Sh. Fouad, P. Tino,: Prototype Based Modelling for Ordinal Classification.
    13th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2012), pp. 208-215, Lecture Notes in Computer Science, Springer-Verlag, LNCS 7435, 2012.

  174. Sh. Fouad, P. Tino, S. Raychaudhury, P. Schneider: Learning Using Privileged Information in Prototype Based Models.
    In Artificial Neural Networks (ICANN 2012), pp. 322-329, Lecture Notes in Computer Science, Springer-Verlag, LNCS 7553, 2012.

  175. M. Gandhi, P. Tino, H. Jaeger: Theory of Input Driven Dynamical Systems.
    20th European Symposium on Artificial Neural Networks - ESANN 2012, i6doc.com, 2012.

  176. P. Tino, A. Rodan: Short Term Memory Quantifications in Input-Driven Linear Dynamical Systems.
    20th European Symposium on Artificial Neural Networks - ESANN 2012, in print, i6doc.com, 2012.

  177. J. Mazgut, M. Pulinyova, P. Tino: Using dimensionality reduction method for binary data to questionnaire analysis.
    In Proceedings of the 7th international conference on Mathematical and Engineering Methods in Computer Science (MEMICS 2011), pp. 146-154, Lecture Notes in Computer Science, Springer-Verlag, LNCS 7119, 2012.

  178. Ph. Weber, P. Tino, B. Bordbar: Process Mining in Non-Stationary Environments.
    20th European Symposium on Artificial Neural Networks - ESANN 2012, in print, i6doc.com, 2012.

  179. N. Nikolaev, P. Tino, N.E. Smirnov: Time-Dependent Series Variance Estimation via Recurrent Neural Networks.
    In Artificial Neural Networks (ICANN 2011), pp. 176-184, Lecture Notes in Computer Science, Springer-Verlag, LNCS 6971, 2011.

  180. Ph. Weber, B. Bordbar, P. Tino, B. Majeed: A Framework for Comparing Process Mining Algorithms.
    In The 6th IEEE GCC Conference, pp. 625-628, IEEE Computer Society, 2011.

  181. Ph. Weber, B. Bordbar, P. Tino: A Principled Approach to the Analysis of Process Mining Algorithms.
    12th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2011), pp. 474-481, Lecture Notes in Computer Science, Springer-Verlag, LNCS 6936, 2011.

  182. A. Rodan, P. Tino: Negatively Correlated Echo State Networks.
    19th European Symposium on Artificial Neural Networks - ESANN 2011, pp. 53-58, i6doc.com, 2011.

  183. P. Tino: One-shot Learning of Poisson Distributions in cDNA Array Analysis.
    In Advances in Neural Networks - Proc. of the 8th International Symposium on Neural Networks - ISNN 2011, pp. 37-46, Lecture Notes in Computer Science, LNCS 6676, Springer-Verlag, 2011.

  184. P. Tino, S.Y. Chong, X.Yao: On Reliability Of Simulations Of Complex Co-Evolutionary Processes.
    In Proc. of the 24th European Conference on Modelling and Simulation - ECMS 2010, pp. 258-264, ECMS, 2010.

  185. J. Magut, P. Tino, M. Boden, H. Yan: Multilinear Decomposition and Topographic Mapping of Binary Tensors.
    In Artificial Neural Networks (ICANN 2010), pp. 317-326, Lecture Notes in Computer Science, Springer-Verlag, LNCS 6352, 2010.

  186. A. Rodan, P. Tino: Simple Deterministically Constructed Recurrent Neural Networks.
    In Intelligent Data Engineering and Automated Learning (IDEAL 2010), pp. 267-274, Lecture Notes in Computer Science, LNCS 6283, Springer-Verlag, 2010.

  187. R. Price, P. Tino: Adapting to NAT timeout values in P2P Overlay Networks.
    In 2010 IEEE International Symposium on Parallel and Distributed Processing (IPDPSW), pp. 1-6, 2010.

  188. N. Gianniotis, P. Tino, S. Spreckley, S. Raychaudhury: Topographic Mapping of Astronomical Light Curves via a Physically Inspired Probabilistic Model.
    In Artificial Neural Networks – ICANN 2009, pp. 567-576, Lecture Notes in Computer Science, LNCS 5768, Springer-Verlag, 2009.

  189. X. Wang, P. Tino, M. Fardal, S. Raychaudhury, A. Babul: Fast Parzen Window Density Estimator.
    In International Joint Conference on Neural Networks - IJCNN 2009, pp. 3267-3274, IEEE Computer Society, 2009.

  190. R. Price, P. Tino: Still Alive: Extending Keep-Alive Intervals in P2P Overlay Networks.
    In 5th International Conference on Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom), pp. 1-10, 2009.

  191. P. Tino, H. Zhao, H. Yan: Probabilistic Model Based Hough Transform for Detection of Co-expression Patterns in Three-Color cDNA Microarray Data.
    In Proceedings of the 2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing - IJCBS 2009, pp. 48-51, IEEE Computer Society, 2009.

  192. X. Wang, P. Tino, M. Fardal: Multiple Manifold Learning Framework based on Hierarchical Mixture Density Model.
    In Machine Learning and Knowledge Discovery in Databases (European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - ECML PKDD 2008), pp. 566-581, Lecture Notes in Computer Science, LNCS 4984, Springer-Verlag, 2008.

  193. M. Cernansky, P. Tino: Predictive Modeling with Echo State Networks .
    In 18th International Conference on Artificial Neural Networks - ICANN 2008, (eds) V. Kurkova, R. Neruda, J. Koutnik. pp. 778-787, Lecture Notes in Computer Science, LNCS 5163, Springer-Verlag, 2008.

  194. P. Tino: Bifurcations of Renormalization Dynamics in Self-organizing Neural Networks.
    In 14th International Conference on Neural Information Processing - ICONIP 2007, (eds) M. Ishikawa et al. pp. 405-414, Lecture Notes in Computer Science, LNCS 4984, Springer-Verlag, 2008.

  195. P. Tino, N. Gianniotis: Metric Properties of Structured Data Visualizations through Generative Probabilistic Modeling.
    In 20th International Joint Conference on Artificial Intelligence - IJCAI'07, (ed.) Manuela M. Veloso. pp. 1083-1088, AAAI Press, 2007.

  196. P. Tino, B. Hammer, M. Boden: Markovian bias of neural-based architectures with feedback connections.
    In Perspectives of Neural-Symbolic Integration, (eds) B. Hammer, P. Hitzler. pp. 95-133, Studies in Computational Intelligence Vol. 77, Springer, 2007.

  197. P. Tino: On Conditions for Intermittent Search in Self-organizing Neural Networks.
    In Advances in Artificial Intelligence - 6th Mexican International Conference on Artificial Intelligence - MICAI 2007, (eds) A. Gelbukh, A. Fernando, K. Morales. pp. 172-181, Lecture Notes in Computer Science (4827),Springer-Verlag, 2007.

  198. M. Cernansky, P. Tino: Comparison of Echo State Networks with Simple Recurrent Networks and Variable-Length Markov Models on Symbolic Sequences .
    In 17th International Conference on Artificial Neural Networks - ICANN 2007, (eds) J. Marques de Sa, L.A. Alexandre, W. Duch, D.P. Mandic. pp. 618-627, Lecture Notes in Computer Science, Springer-Verlag, 2007.

  199. Nikolaos Gianniotis, Peter Tino: Visualisation of tree-structured data through generative probabilistic modelling .
    In 15th European Symposium on Artificial Neural Networks - ESANN 2007. pp. 97-102, 2007.

  200. J.C. Cuevas-Tello, P. Tino, S. Raychaudhury: A kernel-based approach to estimating phase shifts between irregularly sampled time series: an application to gravitational lenses.
    In 17th European Conference on Machine Learning - ECML 2006, (eds) J. Fuernkranz, T. Scheffer, M. Piliopoulou. pp. 614-621, Lecture Notes in Computer Science, Springer-Verlag, 2006.

  201. H. Chen, P. Tino, X. Yao: A Probabilistic Ensemble Pruning Algorithm.
    In 6th IEEE International Conference on Data Mining - ICDM06 - Workshops, (eds) . pp. 878-882, IEEE Computer Society, 2006.

  202. P. Tino: Critical Temperatures for Intermittent Search in Self-Organizing Neural Networks.
    In Parallel Problem Solving from Nature - PPSN IX, (eds) T.P. Runarsson, H-G Beyer, E. Burke, J J. Merelo-Guervos, L. Darrell Whitley, X. Yao. pp. 633-640, Lecture Notes in Computer Science, Springer-Verlag, 2006.

  203. J.M. Binner, B. Jones, G. Kendal, J. Tepper, P. Tino: Does Money Matter? An Artificial Intelligence Approach. .
    In Proceedings of 9th Joint Conference on Information Sciences 2006 (5th International Conference on Computational Intelligence in Economics and Finance), Kaohsiung, Taiwan. pp 72-75, 2006.

  204. P. Tino, I. Farkas, J.van Mourik: Recursive Self-Organizing Map as a Contractive Iterative Function System.
    In Intelligent Data Engineering and Automated Learning - IDEAL 2005, (eds) M. Gallagher, J. Hogan, F. Maire. pp. 327-334, Lecture Notes in Computer Science, Springer-Verlag, 2005.

  205. N. Nikolaev, P. Tino: Sequential Relevance Vector Machine Learning from Time Series.
    In Proc. Int. Joint Conference on Neural Networks - IJCNN 2005, pp. 1308-1313, IEEE, 2005.

  206. P. Tino, A. Mills: Learning Beyond Finite Memory in Recurrent Networks Of Spiking Neurons.
    In Advances in Natural Computation - ICNC 2005, (eds) L. Wang, K. Chen, Y.S. Ong. pp. 666-675, Lecture Notes in Computer Science, Springer-Verlag, 2005.

  207. P. Tino, I. Farkas: On Non-Markovian Topographic Organization of Receptive Fields in Recursive Self-Organizing Map.
    In Advances in Natural Computation - ICNC 2005, (eds) L. Wang, K. Chen, Y.S. Ong. pp. 676-685, Lecture Notes in Computer Science, Springer-Verlag, 2005.

  208. P. Tino, N. Nikolaev, X. Yao : Volatility Forecasting with Sparse Bayesian Kernel Models.
    In Proceedings of 8th Joint Conference on Information Sciences 2005 (4th International Conference on Computational Intelligence in Economics and Finance), Salt Lake City, UT. pp 1150-1153, 2005.

  209. R. Price, P. Tino: Evaluation of Adaptive Nature Inspired Task Allocation Against Alternate Decentralised Multiagent Strategies.
    In Parallel Problem Solving from Nature - PPSN VIII, (eds) X. Yao et al. pp. 982-990, Lecture Notes in Computer Science, Springer-Verlag, 2004.

  210. G. Polcicova, P. Tino: Introducing a star topology into latent class models for collaborative filtering.
    In Proceedings of first IFIP Conference on Artificial Intelligence Applications and Innovations - WCC 2004, (eds) M. Bramer, V. Devedzic. pp. 293-303, Kluwer academic publishers, 2004.

  211. P. Tino, A. Kaban, Y. Sun: A Generative Probabilistic Approach to Visualizing Sets of Symbolic Sequences.
    In Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD-2004, (eds) R. Kohavi, J. Gehrke, W. DuMouchel, J. Ghosh. pp. 701-706, ACM Press, 2004.

  212. P. Tino, Y. Sun, I. Nabney: Semi-Supervised Construction of General Visualization Hierarchies.
    In Proceedings of the 2002 International Conference on Artificial Intelligence - IC-AI'02, (eds) H.R. Arabnia, Y. Mun. pp. 1380-1386, CSREA Press, 2002.

  213. P. Tino, B. Hammer: Architectural Bias in Recurrent Neural Networks - Fractal Analysis.
    In Artificial Neural Networks - ICANN 2002, (ed.) J.R.Dorronsoro. pp. 1359-1364, Lecture Notes in Computer Science, Springer-Verlag, 2002.
    Best Conference Paper Award (European Neural Network Society, 2002)

  214. A. Kaban, P. Tino, M. Girolami: A General Framework for a Principled Hierarchical Visualization of Multivariate Data.
    In International Conference on Intelligent Data Engineering and Automated Learning - IDEAL 2002, pp. 17-23, Lecture Notes in Computer Science, Springer-Verlag, 2002.

  215. Y. Sun, P. Tino, I. Nabney: Visualization of incomplete data using class information constraints.
    In Uncertainty in Geometric Computations, (eds) J. Winkler, M. Niranjan. pp. 165-174, Kluwer, 2002.

  216. P. Tino, M. Cernansky, L. Benuskova: Markovian Architectural Bias of Recurrent Neural Networks .
    In Intelligent Technologies - Theory and Applications. Frontiers in AI and Applications , vol. 76, (eds) P. Sincak, J. Vascak, V. Kvasnicka and J. Pospichal. pp. 17-23, IOS Press, Amsterdam, 2002.

  217. P. Tino, I. Nabney, Yi Sun, B.S. Williams: A Principled Approach to Interactive Hierarchical Non-Linear Visualization of High-Dimensional Data.
    In Computing Science and Statistics, Volume 33: Frontiers in Data Mining and Bioinformatics. Proceedings of the 33rd Symposium on the Interface. (eds) E.J. Wegman, A. Braverman, A. Goodman, P. Smyth. pp. 580-587, Interface Foundation of North America, 2002.

  218. P. Tino, I. Nabney, Yi Sun: Using Directional Curvatures to Visualize Folding Patterns of the GTM Projection Manifolds.
    In Artificial Neural Networks - ICANN 2001, (eds) G. Dorffner, H. Bischof and K. Hornik. pp. 421-428, Lecture Notes in Computer Science, Springer-Verlag, 2001.

  219. P. Tino, Ch. Schittenkopf, G. Dorffner: Methods of Symbolic Dynamics in Options Trading.
    In Proceedings of Computational Finance 2000, London, UK (on CD).

  220. P. Tino, M. Stancik, L. Benuskova: Building predictive models on complex symbolic sequences with a second-order recurrent BCM network with lateral inhibition.
    In Proceedings of the IEEE-INNS-ENNS Int. Joint Conference on Neural Networks, Como, Italy. Vol. 2, pp. 265-270, 2000.

  221. P. Tino, M. Stancik, L. Benuskova: Building predictive models on complex symbolic sequences via a first-order recurrent BCM network with lateral inhibition.
    In Quo Vadis Computational Intelligence? New Trends and Approaches in Computational Intelligence, (eds) P. Sincak and J. Vascak. pp. 42-50, Physica-Verlag, Heidelberg, 2000.

  222. Ch. Schittenkopf, P. Tino, G. Dorffner: The profitability of trading volatility using real-valued and symbolic models.
    Proceedings of the IEEE/IAFE conference on Computational Inteligence in Financial Engineering (CIFEr 2000), New York City, NY, USA. pp. 8-11, 2000.

  223. Sh. Parfitt, P. Tino, G. Dorffner: Graded grammaticality in Prediction Fractal Machines.
    In Advances in Neural Information Processing Systems 12, (eds) S. A. Solla, T. K. Leen, K-R. Müller. pp. 52-58, MIT Press, 2000.

  224. P. Tino, G. Dorffner: Building predictive models from spatial representations of symbolic sequences .
    In Advances in Neural Information Processing Systems 12, (eds) S. A. Solla, T. K. Leen, K-R. Müller. pp. 645-651, MIT Press, 2000.

  225. P. Tino, G. Dorffner, Ch. Schittenkopf: Understanding State Space Organization in Recurrent Neural Networks with Iterative Function Systems Dynamics.
    In Hybrid Neural Symbolic Integration, (eds) S. Wermter, R. Sun. pp. 256-270, Springer Verlag, 2000.

  226. P. Tino, Ch. Schittenkopf, G. Dorffner, E.J. Dockner: A Symbolic Dynamics Approach to Volatility Prediction.
    In Computational Finance, (eds) Y.S. Abu-Mostafa, B. LeBaron, A.W. Lo, A.S. Weigend. pp. 137-151, MIT Press, Cambridge, MA, 2000.

  227. T. Lin, B.G. Horne, P. Tino, C.L. Giles: Learning Long-Term Dependencies in NARX Recurrent Neural Networks.
    In Recurrent Neural Networks - Design and Applications, (eds) L.R. Medsker, L.C. Jain. pp. 133-152, CRC Press, 1999.

  228. P. Tino, B.G. Horne, C.L. Giles, P.C. Collingwood: Finite State Machines and Recurrent Neural Networks - Automata and Dynamical Systems Approaches.
    In Neural Networks and Pattern Recognition, (eds) J.E. Dayhoff, O. Omidvar. pp. 171-220, Academic Press, 1998.

  229. P. Tino, V. Vojtek: Modeling complex sequences with recurrent neural networks.
    In Artificial Neural Networks and Genetic Algorithms, (eds) G.D. Smith, N.C. Steele, R.F. Albrecht. pp. 459-463, Springer Verlag, 1998.

  230. P. Tino, V. Vojtek: Extracting stochastic machines from recurrent neural networks trained on complex symbolic sequences.
    In Proceedings of the first International Conference on Knowledge-Based Intelligent Electronic Systems, pp. 284-302, vol. 2, 1997.

  231. P. Tino, V. Vojtek: Spatial Representation of Temporal Structure in Symbolic Sequences through Iterated Function Systems.
    In Proceedings of the International Conference on Measurement (MEASUREMENT'97), (eds) Ivan Frollo, Anna Plackova. 1997.

  232. T. Lin, B.G. Horne, P. Tino, C.L. Giles: Learning long-term dependencies is not as difficult with NARX recurrent networks.
    In Advances in Neural Information Processing Systems 8, (eds) D.S. Touretzky, M.C. Mozer, M.E. Hasselmo. pp. 577-602, MIT Press, 1996.

  233. P. Tino, M. Koteles: Modeling Complex Symbolic Sequences with Recurrent Neural Networks.
    In Proceedings of the 1-st Slovak Neural Network Symposium, pp. 78-85, 1996.

  234. P. Tino, B.G. Horne, C.L. Giles: Stability and bifurcations analysis of fixed points in discrete time recurrent neural networks with two neurons.
    In Proceedings of the World Congress on Neural Networks, Washington D.C., Vol 3, pp. 170-173, 1995.

  235. T. Lin, B.G. Horne, P. Tino, C.L. Giles: Long-term dependencies in NARX networks.
    In Proceedings of the World Congress on Neural Networks, Washington D.C., Vol 3, pp. 142-146, 1995.

  236. P. Tino, I.E. Jelly, V. Vojtek: Non-Standard Topologies of Neuron Field in Self-Organizing Feature Maps.
    In Proceedings of the AIICSR'94 conference, pp. 391-396, World Scientific Publishing Company, 1994.



This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders, usually the publishers. All persons and robots copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.



Technical reports




Talks

  • Joint Modelling of Behavioural and Brain Imaging Data

  • Recurrent Neural Networks as Parametrized Non-autonomous Dynamical Systems A Machine Learning Perspective

  • Machine Learning in Astrophysics - Making Sense of Simulation Data Through Probabilistic Multi-Manifold Learning

  • From Dynamical Systems to Kernel Based Feature Spaces and Back - Dynamical Systems as Temporal Feature Spaces

  • Dynamical Systems as Feature Representations for Learning from Temporal Data

  • Probabilistic Modelling in Machine Learning

  • State Space Models in Machine Learning (Research Student Training)

  • Probabilistic Modelling in Machine Learning: topographic mappings

  • Learning from Temporal Data through Learning in the Space of Dynamical Systems (non-parametric case)

  • Fisher Memory of linear Wigner Echo State Networks

  • Learning in the Model Space for Sequential Data - from Temporal Filters to Sequence Kernels and Back

  • Machine Intelligence in Astronomy - Win-Win Situation

  • Learning from Temporal Data Using Dynamical Feature Space

  • State Space Models - a Personal View
  • One-shot Learning of Poisson Distributions - Information Theory of Audic-Claverie Statistic for Analyzing cDNA Arrays
  • Probabilistic Model Based Hough Transform for Detection of Co-expression Patterns in Three-Color cDNA Microarray Data
  • Critical Temperatures for Intermittent Search in Self-Organizing Neural Networks

  • Topographic Mapping and Dimensionality Reduction of Binary Tensor Data of Arbitrary Rank

  • Does Money Matter? - An Artificial Intelligence Approach

  • Visualizing multivariate data

  • From Symbolic Sequences to Fractals and Back: Markovian Architectural Bias of Recurrent Neural Networks

  • Probabilistic Framework for Model-Based Topographic Map Formation

  • A Probabilistic Modelling Approach to Topographic Mapping of Structured Data

  • Latent Space Modeling in Collaborative Filtering

  • Volatility Forecasting with Sparse Bayesian Kernel Models

  • Topographic Organization of Receptive Fields in RecSOM or RecSOM as nonlinear IFS

  • Learning Beyond Finite Memory in Recurrent Networks Of Spiking Neurons

  • Neural Network Applications

  • Model-Based Clustering and Topographic Map Formation of High-Dimensional and Structured Data

  • Machine Learning and Computational Finance - 2 case studies

  • Fool's gold? On the use and abuse of Machine Learning

  • Time Delay Estimation in Gravitational Lensing

  • Can we get the machines to learn from experience?

  • Probabilistic Modelling in Machine Learning - Applications in Astronomy and Astrophysics

  • A View of Reservoirs as State Space Models

  • CS at Birmingham

  • Inaugural



    [back to my homepage ]