Information Theoretic Learning Renyi's Entropy and Kernel Perspectives /

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...

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Détails bibliographiques
Cote:Libro Electrónico
Auteur principal: Principe, Jose C. (Auteur)
Collectivité auteur: SpringerLink (Online service)
Format: Électronique eBook
Langue:Inglés
Publié: New York, NY : Springer New York : Imprint: Springer, 2010.
Édition:1st ed. 2010.
Collection:Information Science and Statistics,
Sujets:
Computer science.
Artificial intelligence.
Signal processing.
Computational intelligence.
Statistics .
Geographic information systems.
Theory of Computation.
Artificial Intelligence.
Signal, Speech and Image Processing .
Computational Intelligence.
Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
Geographical Information System.
Accès en ligne:Texto Completo
Table des matières:
  • 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.

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