Publications of Peter Tino
Publications
  of
  Peter Tino



    Journal Papers

  1. A. Rodan, P. Tino: Simple Deterministically Constructed Cycle Reservoirs with Regular Jumps.
    Neural Computation, accepted, 2012 (c) MIT Press

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

  3. 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, accepted, 2012. (c) IEEE

  4. 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

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

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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

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

  12. 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)

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

  14. 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

  15. 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

  16. 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)

  17. 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

  18. 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

  19. 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

  20. 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

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

  22. 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

  23. 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

  24. 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

  25. 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

  26. 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

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

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

  29. 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

  30. 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

  31. 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

  32. 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

  33. 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

  34. 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

  35. 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

  36. 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

  37. 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

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

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

  40. 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)

  41. 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

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

    Books

  43. 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.

  44. 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.

  45. 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.

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

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

    Book Chapters and Refereed Conference Papers

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

  49. 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.

  50. 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.

  51. 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.

  52. 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.

  53. 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.

  54. 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.

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

  56. 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.

  57. 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.

  58. 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.

  59. 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.

  60. 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.

  61. 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.

  62. 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.

  63. 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.

  64. 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.

  65. 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.

  66. 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.

  67. 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.

  68. 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.

  69. 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.

  70. 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.

  71. 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.

  72. 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.

  73. 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.

  74. 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.

  75. 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.

  76. 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.

  77. 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.

  78. 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.

  79. 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.

  80. 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.

  81. 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.

  82. 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.

  83. 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.

  84. 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.

  85. 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.

  86. 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)

  87. 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.

  88. 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.

  89. 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.

  90. 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.

  91. 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.

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

  93. 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.

  94. 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.

  95. 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.

  96. 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.

  97. 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.

  98. 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.

  99. 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.

  100. 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.

  101. 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.

  102. 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.

  103. 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.

  104. 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.

  105. 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.

  106. 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.

  107. 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.

  108. 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.

  109. 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.


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Technical reports



Talks

  • One-shot Learning of Poisson Distributions - Information Theory of Audic-Claverie Statistic for Analyzing cDNA Arrays

  • 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

  • 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

  • CS at Birmingham


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