Information Theoretic Learning by Jose C Principe
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.-
Pattern Recognition and Machine Learning
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Bayesian Networks and Decision Graphs
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Novelty, Information and Surprise
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Computational Methods in Biometric Authentication
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Statistical Image Processing and Multidimensional Modeling
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The Practice of Time Series Analysis
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Computer Intrusion Detection and Network Monitoring
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Probabilistic Networks and Expert Systems
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Estimation of Dependences Based on Empirical Data
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Probabilistic Conditional Independence Structures
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Information and Complexity in Statistical Modeling
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Feedforward Neural Network Methodology
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Statistical and Inductive Inference by Minimum Message Length
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The Cross-Entropy Method
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Cumulative Sum Charts and Charting for Quality Improvement
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Nonlinear Dimensionality Reduction
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Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis
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Support Vector Machines
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The Nature of Statistical Learning Theory
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Sequential Monte Carlo Methods in Practice
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 clusteringStudents of engineering and statistics will greatly benefit by reading it.” (C. R. Rao, Technometrics, Vol. 55 (1), February, 2013)
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 Reviews on Biomedical Engineering. 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.
| SKU | Unavailable |
| ISBN 13 | 9781461425854 |
| ISBN 10 | 1461425859 |
| Title | Information Theoretic Learning |
| Author | Jose C Principe |
| Series | Information Science And Statistics |
| Condition | Unavailable |
| Binding Type | Paperback |
| Publisher | Springer-Verlag New York Inc. |
| Year published | 2012-05-27 |
| Number of pages | 448 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |