دانلود کتاب Information Theoretic Learning: Renyi's Entropy and Kernel Perspectives (Information Science and Statistics)

فهرست مطالب :

Information Theoretic
Learning
Preface
1 Information Theory, Machine Learning, and Reproducing Kernel Hilbert Spaces
2 Renyi\'s Entropy, Divergence and Their Nonparametric Estimators
3 Adaptive Information Filtering with Error Entropy and Error Correntropy Criteria
4 Algorithms for Entropy and Correntropy Adaptation with Applications to Linear Systems
5 Nonlinear Adaptive Filtering with MEE, MCC, and Applications
6 Classification with EEC, Divergence Measures, and Error Bounds
7 Clustering with ITL Principles
8 Self-Organizing ITL Principles for Unsupervised Learning
9 A Reproducing Kernel Hilbert Space Framework for ITL
10 Correntropy for Random Variables: Properties and Applications in Statistical Inference
11 Correntropy for Random Processes: Properties and Applications in Signal Processing
A PDF Estimation Methods and Experimental Evaluation of ITL Descriptors
Bibliography
Index
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