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Information Theoretic Learning: Renyi's Entropy and Kernel Perspectives
Jose C. Principe
€ 260.15
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Description for Information Theoretic Learning: Renyi's Entropy and Kernel Perspectives
hardcover. 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. Series: Information Science and Statistics. Num Pages: 448 pages, biography. BIC Classification: UYA; UYQ. Category: (P) Professional & Vocational; (U) Tertiary Education (US: College). Dimension: 164 x 237 x 33. Weight in Grams: 968.
This bookisan outgrowthoften yearsof researchatthe Universityof Florida Computational NeuroEngineering Laboratory (CNEL) in the general area of statistical signal processing and machine learning. One of the goals of writing the book is exactly to bridge the two ?elds that share so many common problems and techniques but are not yet e?ectively collaborating. Unlikeotherbooks thatcoverthe state ofthe artinagiven?eld,this book cuts across engineering (signal processing) and statistics (machine learning) withacommontheme:learningseenfromthepointofviewofinformationt- orywithanemphasisonRenyi'sde?nitionofinformation.Thebasicapproach is to utilize the information theory descriptors of entropy and divergence as nonparametric cost functions for the design of adaptive systems in unsup- vised or supervised training modes. Hence the title: Information-Theoretic ... Read more
This bookisan outgrowthoften yearsof researchatthe Universityof Florida Computational NeuroEngineering Laboratory (CNEL) in the general area of statistical signal processing and machine learning. One of the goals of writing the book is exactly to bridge the two ?elds that share so many common problems and techniques but are not yet e?ectively collaborating. Unlikeotherbooks thatcoverthe state ofthe artinagiven?eld,this book cuts across engineering (signal processing) and statistics (machine learning) withacommontheme:learningseenfromthepointofviewofinformationt- orywithanemphasisonRenyi'sde?nitionofinformation.Thebasicapproach is to utilize the information theory descriptors of entropy and divergence as nonparametric cost functions for the design of adaptive systems in unsup- vised or supervised training modes. Hence the title: Information-Theoretic ... Read more
Product Details
Format
Hardback
Publication date
2010
Publisher
Springer United States
Number of pages
448
Condition
New
Series
Information Science and Statistics
Number of Pages
448
Place of Publication
New York, NY, United States
ISBN
9781441915696
SKU
V9781441915696
Shipping Time
Usually ships in 15 to 20 working days
Ref
99-15
About Jose C. Principe
José C. Principe is Distinguished Professor of Electrical and Biomedical Engineering, and BellSouth Professor at the University of Florida, and the Founder and Director of the Computational NeuroEngineering Laboratory. He is an IEEE and AIMBE Fellow, Past President of the International Neural Network Society, Past Editor-in-Chief of the IEEE Trans. on Biomedical Engineering and the Founder Editor-in-Chief of the IEEE ... Read more
Reviews for Information Theoretic Learning: Renyi's Entropy and Kernel Perspectives
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), ... Read more