Research Interests of Peter Tino
Research Interests of Peter Tino
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Areas of Interest
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Artificial Intelligence,
Machine Learning,
Neural Networks,
Evolutionary Computation,
Bioinformatics,
Computational Biology
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Intelligent Methods for Pattern Recognition,
Financial Forecasting, Molecular Biology
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Knowledge Representation in Neural Networks, Integration of Symbolic and
Connectionist Paradigms, Hybrid Systems
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Computational Models of Dynamical Systems,
Evolution of Complexity in Self-Organizing Systems
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Fractal and Multifractal Analysis
Past and Present Research Topics
- Recurrent neural networks
- their ability to represent and learn temporal structures in (mainly symbolic) data
- analysis of training process
- interpretation of induced knowledge using automata,
dynamical systems and information theories
- relationship to other adaptive and learning paradigms
- Geometric representations of symbolic sequences
- mapping symbolic structures into vector metric spaces
- multifractal properties of representations based on iterative function systems
- relationship to recurrent neural networks
- constructing Markov-like predictive models on fractal representations of symbolic streams
- applications in Bioinformatics and language modeling
- Time series modeling and prediction through quantization
- quantization techniques
- symbolic modeling and prediction tools: Markov models, Hidden Markov
models, Variable memory length Markov models, Prediction fractal machines,
Epsilon machines, Formal grammars, ...
- applications in finance
- comparison with non-symbolic methods
- Modeling and prediction of financial time series
- modeling hidden dynamics in returns of financial derivatives (DJIA, DAX, FTSE)
- state space models of conditional mean and variance
- extracting abstract rules from financial time series quantized into symbolic streams
- building automatic trading strategies for option markets
- Hierarchical visualization of high-dimensional data
- principled hierarchical visualization, where each visualization plot
is a (local) probabilistic model of data
- magnification factors and directional curvatures
as a means for understanding properties of projection manifolds
- applications in bioinformatics and document mining
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