Information Theoretic Learning

1. Information Theory, Machine Learning, and Reproducing Kernel Hilbert Spaces
José C. Principe

2. Renyi’s Entropy, Divergence and Their Nonparametric Estimators
Dongxin Xu, Deniz Erdogmuns

3. Adaptive Information Filtering with Error Entropy and Error Correntropy Criteria
Deniz Erdogmus, Weifeng Liu

4. Algorithms for Entropy and Correntropy Adaptation with Applications to Linear Systems
Deniz Erdogmus, Seungju Han, Abhishek Singh

5. Nonlinear Adaptive Filtering with MEE, MCC, and Applications
Deniz Erdogmus, Rodney Morejon, Weifeng Liu

6. Classification with EEC, Divergence Measures, and Error Bounds
Deniz Erdogmus, Dongxin Xu, Kenneth Hild

7. Clustering with ITL Principles
Robert Jenssen, Sudhir Rao

8. Self-Organizing ITL Principles for Unsupervised Learning
Sudhir Rao, Deniz Erdogmus, Dongxin Xu, Kenneth Hild

9. A Reproducing Kernel Hilbert Space Framework for ITL
Jianwu Xu, Robert Jenssen, Antonio Paiva, Il Park

10. Correntropy for Random Variables: Properties and Applications in Statistical Inference
Weifeng Liu, Puskal Pokharel, Jianwu Xu, Sohan Seth

11. Correntropy for Random Processes: Properties and Applications in Signal Processing
Puskal Pokharel, Ignacio Santamaria, Jianwu Xu, Kyu-hwa Jeong, Weifeng Liu

Avainsanat: Engineering, Signal, Image and Speech Processing, Computational Intelligence, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences, Remote Sensing/Photogrammetry