Posted by **ChrisRedfield** at July 29, 2013

Published: 2012-11-10 | ISBN: 1461450756 | PDF | 202 pages | 3 MB

Posted by **avava** at June 3, 2013

ISBN: 1461450756 | 2012 | PDF | 200 pages | 5.1 MB

Posted by **avava** at May 24, 2011

Publisher: Springer | ISBN 10: 3642198953 | 2011 | PDF | 149 pages | 2 MB

Posted by **zolao** at Aug. 10, 2013

English | ISBN: 3642378455 | 2013 | 270 pages | PDF | 19 MB

For many engineering problems we require optimization processes with dynamic adaptation as we aim to establish the dimension of the search space where the optimum solution resides and develop robust techniques to avoid the local optima usually associated with multimodal problems. This book explores multidimensional particle swarm optimization, a technique developed by the authors that addresses these requirements in a well-defined algorithmic approach.

Posted by **nebulae** at July 25, 2013

English | ISBN: 3642378455 | 2013 | 270 pages | PDF | 19 MB

Posted by **Specialselection** at March 18, 2013

English | 2009-08-26 | ISBN: 1439803692 | 338 pages | PDF | 4.3 mb

Posted by **tot167** at March 30, 2010

CRC Press | 2009 | ISBN: 1439803692 | 349 pages | PDF | 10,9 MB

Posted by **foosaa** at July 22, 2009

Springer | 2007 | ISBN: 0387310738 | English | 738 Pages | PDF | 9.5 MB

Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation. Similarly, new models based on kernels have had significant impact on both algorithms and applications. This new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first year PhD students, as well as researchers and practitioners, and assumes no previous knowledge of pattern recognition or machine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

Posted by **roxul** at Nov. 6, 2016

English | Sep 15, 2011 | ISBN: 1607507692 | 378 Pages | PDF | 1 MB

Posted by **arundhati** at July 22, 2016

2015 | ISBN-10: 1518678645 | 106 pages | Djvu | 1 MB