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portada Information Theoretic Learning: Renyi's Entropy and Kernel Perspectives
Information Theoretic Learning: Renyi's Entropy and Kernel PerspectivesInformation Theoretic Learning: Renyi's Entropy and Kernel PerspectivesInformation Theoretic Learning: Renyi's Entropy and Kernel PerspectivesInformation Theoretic Learning: Renyi's Entropy and Kernel PerspectivesInformation Theoretic Learning: Renyi's Entropy and Kernel PerspectivesInformation Theoretic Learning: Renyi's Entropy and Kernel Perspectives
Type
Physical Book
Author
Publisher
Language
English
Pages
448
Format
Hardcover
Dimensions
23.4x15.6x3 cm
Weight
0.94 kg.
ISBN
9781441915696
ISBN13
9781441915696
Categories

Information Theoretic Learning: Renyi's Entropy and Kernel Perspectives

José C. Principe (Author) · Springer · Hardcover

Information Theoretic Learning: Renyi's Entropy and Kernel Perspectives - Principe, Jose C.

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Synopsis "Information Theoretic Learning: Renyi's Entropy and Kernel Perspectives"

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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