Information Theory and Statistical Learning

1. Algorithmic Probability: Theory and Applications
Ray J. Solomonoff

2. Model Selection and Testing by the MDL Principle
Jorma Rissanen

3. Normalized Information Distance
Paul M. B. Vitányi, Frank J. Balbach, Rudi L. Cilibrasi, Ming Li

4. The Application of Data Compression-Based Distances to Biological Sequences
Attila Kertesz-Farkas, Andras Kocsor, Sandor Pongor

5. MIC: Mutual Information Based Hierarchical Clustering
Alexander Kraskov, Peter Grassberger

6. A Hybrid Genetic Algorithm for Feature Selection Based on Mutual Information
Jinjie Huang, Panxiang Rong

7. Information Approach to Blind Source Separation and Deconvolution
Pham Dinh-Tuan

8. Causality in Time Series: Its Detection and Quantification by Means of Information Theory
Katerina Hlavácková-Schindler

9. Information Theoretic Learning and Kernel Methods
Robert Jenssen

10. Information-Theoretic Causal Power
Kevin B. Korb, Lucas R. Hope, Erik P. Nyberg

11. Information Flows in Complex Networks
João Barros

12. Models of Information Processing in the Sensorimotor Loop
Daniel Polani, Marco Möller

13. Information Divergence Geometry and the Application to Statistical Machine Learning
Shinto Eguchi

14. Model Selection and Information Criterion
Noboru Murata, Hyeyoung Park

15. Extreme Physical Information as a Principle of Universal Stability
B. Roy Frieden

16. Entropy and Cloning Methods for Combinatorial Optimization, Sampling and Counting Using the Gibbs Sampler
Reuven Rubinstein

Avainsanat: COMPUTERS / Computer Science COM014000