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## Details

Material Type: | Internet resource |
---|---|

Document Type: | Book, Internet Resource |

All Authors / Contributors: |
J C Príncipe |

ISBN: | 9781441915696 1441915699 1441915702 9781441915702 |

OCLC Number: | 502034127 |

Description: | xxii, 526 pages : illustrations ; 24 cm. |

Contents: | 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. |

Series Title: | Information science and statistics. |

Responsibility: | José C. Principe. |

More information: |

### Abstract:

This book is the first cohesive treatment of ITL algorithms to adapt linear or nonlinear learning machines both in supervised and unsupervised paradigms. It compares the performance of ITL algorithms with the second order counterparts in many applications.
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## Reviews

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Publisher Synopsis

From the book reviews:"The book is remarkable in various ways in the information it presents on the concept and use of entropy functions and their applications in signal processing and solution of statistical problems such as M-estimation, classification, and clustering. Students of engineering and statistics will greatly benefit by reading it." (C. R. Rao, Technometrics, Vol. 55 (1), February, 2013) Read more...

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