Information Theoretic Learning Renyi's Entropy and Kernel Perspectives /

• Main Author: Principe, Jose C. (Author)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic
• Language: English
• Published: New York, NY : Springer New York : Imprint: Springer, 2010.
• Series: Information Science and Statistics,
• Subjects: Engineering.; Remote sensing.; Signal, Image and Speech Processing.; Computational Intelligence.; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.; Remote Sensing/Photogrammetry.
• Online Access: https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.1007/978-1-4419-1570-2

This book presents the first cohesive treatment of Information Theoretic Learning (ITL) algorithms to adapt linear or nonlinear learning machines both in supervised or unsupervised paradigms. ITL is a framework where the conventional concepts of second order statistics (covariance, L2 distances, cor...

Table of Contents:

• Information theory, machine learning and reproducing kernel Hilbert spaces
• Renyi<U+0019>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
• Appendix A: PDF estimation methods and experimental evaluation of ITL descriptors.