Information Theory, Machine Learning, and Reproducing Kernel Hilbert
Spaces.- Renyi's Entropy, Divergence and Their Nonparametric
Estimators.- Adaptive Information Filtering with Error Entropy and Error
Correntropy Criteria.- Algorithms for Entropy and Correntropy Adaptation
with Applications to Linear Systems.- Nonlinear Adaptive Filtering with
MEE, MCC, and Applications.- Classification with EEC, Divergence
Measures, and Error Bounds.- Clustering with ITL Principles.-
Self-Organizing ITL Principles for Unsupervised Learning.- A Reproducing
Kernel Hilbert Space Framework for ITL.- Correntropy for Random
Variables: Properties and Applications in Statistical Inference.-
Correntropy for Random Processes: Properties and Applications in Signal
Processing.