Information Theoretic Learning: Renyi's Entropy and Kernel Perspectives (Information Science and Statistics)
| Author | : | |
| Rating | : | 4.38 (753 Votes) |
| Asin | : | 1441915699 |
| Format Type | : | paperback |
| Number of Pages | : | 448 Pages |
| Publish Date | : | 2017-02-28 |
| Language | : | English |
DESCRIPTION:
José C. He has written an interactive electronic book on Neural Networks, a book on Brain Machine Interface Engineering and more recently a book on Kernel Adaptive Filtering, and was awarded the 2011 IEEE Neural Network Pioneer Award.. on Biomedical Engineering and the Founder Editor-in-Chief of the IEEE Reviews on Biomedical Engineering
It compares the performance of ITL algorithms with the second order counterparts in many applications.. This book is the first cohesive treatment of ITL algorithms to adapt linear or nonlinear learning machines both in supervised and unsupervised paradigms
"Brilliant idea but not well-presented" according to PretendToBeSherlock. The ideas in this book are amazing. However, the presentation of the ideas are really lousy. The train of thought is messy, the authors just put all their math and their results there without a clear organization. It is like randomly writing words which are relevant to a theme. No matter how great the theme is, these words do not make a poem.. Descriptive matter This product, Information Theoretic Learning, brings a set of useful representations of combined learning-algorithms with optimal property and practical information theory with signal processing approach.
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. Rao, Technometrics, Vol. 55 (1), February, 2013). R. Students of engineering and statistics will greatly benefit by reading it.” (C