Information theoretic learning with adaptive kernels

DOI10.1016/J.SIGPRO.2010.06.023zbMath1203.94001OpenAlexW2087558162MaRDI QIDQ612575

Abhishek Singh, Jose C. Principe

Publication date: 29 December 2010

Published in: Signal Processing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.sigpro.2010.06.023

zbMATH Keywords

minimum error entropyadaptive system traininginformation theoretic learningkernel widthKullback Leibler divergence

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Measures of information, entropy (94A17) Information theory (general) (94A15) Software, source code, etc. for problems pertaining to information and communication theory (94-04)

Related Items (3)

The C-loss function for pattern classification ⋮ Regularized discriminant entropy analysis ⋮ Block-sparsity regularized maximum correntropy criterion for structured-sparse system identification

Cites Work

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• On Kullback-Leibler loss and density estimation
• Histogram bin width selection for time-dependent Poisson processes
• A Method for Selecting the Bin Size of a Time Histogram
• On the Choice of Smoothing Parameters for Parzen Estimators of Probability Density Functions
• Deterministic Nonperiodic Flow
• On Estimation of a Probability Density Function and Mode

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