Information Theoretic Learning Renyi's Entropy and Kernel Perspectives /

• Κύριος συγγραφέας: Principe, Jose C. (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: New York, NY : Springer New York : Imprint: Springer, 2010.
• Σειρά: Information Science and Statistics,
• Θέματα: Computer science.; Computers.; Artificial intelligence.; Remote sensing.; Statistics.; Computational intelligence.; Computer Science.; Theory of Computation.; Artificial Intelligence (incl. Robotics).; Signal, Image and Speech Processing.; Computational Intelligence.; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.; Remote Sensing/Photogrammetry.

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...

Πίνακας περιεχομένων:

• 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.