Information Theoretic Learning
Title | Information Theoretic Learning PDF eBook |
Author | Jose C. Principe |
Publisher | Springer Science & Business Media |
Total Pages | 538 |
Release | 2010-04-06 |
Genre | Computers |
ISBN | 1441915702 |
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.
Information Theory and Statistical Learning
Title | Information Theory and Statistical Learning PDF eBook |
Author | Frank Emmert-Streib |
Publisher | Springer Science & Business Media |
Total Pages | 443 |
Release | 2009 |
Genre | Computers |
ISBN | 0387848150 |
This interdisciplinary text offers theoretical and practical results of information theoretic methods used in statistical learning. It presents a comprehensive overview of the many different methods that have been developed in numerous contexts.
Information Theory, Inference and Learning Algorithms
Title | Information Theory, Inference and Learning Algorithms PDF eBook |
Author | David J. C. MacKay |
Publisher | Cambridge University Press |
Total Pages | 694 |
Release | 2003-09-25 |
Genre | Computers |
ISBN | 9780521642989 |
Information theory and inference, taught together in this exciting textbook, lie at the heart of many important areas of modern technology - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics and cryptography. The book introduces theory in tandem with applications. Information theory is taught alongside practical communication systems such as arithmetic coding for data compression and sparse-graph codes for error-correction. Inference techniques, including message-passing algorithms, Monte Carlo methods and variational approximations, are developed alongside applications to clustering, convolutional codes, independent component analysis, and neural networks. Uniquely, the book covers state-of-the-art error-correcting codes, including low-density-parity-check codes, turbo codes, and digital fountain codes - the twenty-first-century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, the book is ideal for self-learning, and for undergraduate or graduate courses. It also provides an unparalleled entry point for professionals in areas as diverse as computational biology, financial engineering and machine learning.
Information-Theoretic Methods in Data Science
Title | Information-Theoretic Methods in Data Science PDF eBook |
Author | Miguel R. D. Rodrigues |
Publisher | Cambridge University Press |
Total Pages | 561 |
Release | 2021-04-08 |
Genre | Computers |
ISBN | 1108427138 |
The first unified treatment of the interface between information theory and emerging topics in data science, written in a clear, tutorial style. Covering topics such as data acquisition, representation, analysis, and communication, it is ideal for graduate students and researchers in information theory, signal processing, and machine learning.
An Information-Theoretic Approach to Neural Computing
Title | An Information-Theoretic Approach to Neural Computing PDF eBook |
Author | Gustavo Deco |
Publisher | Springer Science & Business Media |
Total Pages | 265 |
Release | 2012-12-06 |
Genre | Computers |
ISBN | 1461240166 |
A detailed formulation of neural networks from the information-theoretic viewpoint. The authors show how this perspective provides new insights into the design theory of neural networks. In particular they demonstrate how these methods may be applied to the topics of supervised and unsupervised learning, including feature extraction, linear and non-linear independent component analysis, and Boltzmann machines. Readers are assumed to have a basic understanding of neural networks, but all the relevant concepts from information theory are carefully introduced and explained. Consequently, readers from varied scientific disciplines, notably cognitive scientists, engineers, physicists, statisticians, and computer scientists, will find this an extremely valuable introduction to this topic.
Information Theoretic Learning
Title | Information Theoretic Learning PDF eBook |
Author | Jose C. Principe |
Publisher | Springer |
Total Pages | 448 |
Release | 2010-04-15 |
Genre | Computers |
ISBN | 9781441915696 |
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.
Information Theoretic Learning
Title | Information Theoretic Learning PDF eBook |
Author | |
Publisher | Springer |
Total Pages | 538 |
Release | 2010 |
Genre | |
ISBN | 9781441915733 |