Information Theoretic Learning

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Welcome to Information Theoretic Learning

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ITL Brief Introduction

Information Theoretic Learning (ITL) was initiated in the late 90’s at CNEL and has been a center piece of the research effort. ITL uses descriptors from information theory (entropy and divergences) estimated directly from the data to substitute the conventional statistical descriptors of variance and covariance. ITL can be used in the adaptation of linear or nonlinear filters and also in unsupervised and supervised machine learning applications. See the ITL Resource Center for tutorials, examples and Matlab code.
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Current Projects

  1. Correntropy Dependence Measure
  2. Nonlinearity tests based on Correntropy
  3. Pitch Detection Based on Correntropy
  4. Nonlinear Granger Causality based on correntropy
  5. Compressive sampling based on correntropy
  6. ITL Feature Extraction for mine recognition
  7. The Principle of Relevant Entropy
  1. On-line KLMS is intrinsically regularized
  2. Nonlinear adaptive filters in RKHS
  3. Active Learning Strategies

Invited Talks

Related Publications

Personal tools
EEL 6935