Posted by **nebulae** at May 20, 2015

English | ISBN: 9814335452 | 2015 | 404 pages | PDF | 2 MB

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 **exLib** at Nov. 21, 2011

Sрringеr Science+Business Media | 2006 | ISBN: 0387310732 0387310738 | 101 pages | PDF | 1 MB

Example solutions for a subset of the exercises are available from the book. This issue is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information. A volume deals with practical aspects of pattern recognition and machine learning, and will include free software implementations of the key algorithms along with example data sets.

Posted by **exLib** at Nov. 21, 2011

Sрringеr Science+Business Media | 2006 | ISBN: 0387310738 9780387310732 | 761 pages | PDF | 5 MB

This textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

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 **lout** at July 1, 2011

Publisher: Sp..ring..er 2011 | 483 Pages | ISBN: 3642217850 | PDF | 9 MB

Posted by **interes** at Jan. 10, 2017

English | ISBN: 1461453224 | 2013 | PDF | 207 pages | 7 MB

Posted by **Rare-1** at Dec. 17, 2015

Publisher: Springer | English | 2015 | ISBN:3319193899 | 753 pages | PDF | 112 MB

Posted by **andr1078** at July 19, 2015

Publisher: Springer | English | 2015 | ISBN:3319193899 | 753 pages | PDF | 112 MB

This book constitutes the proceedings of the 7th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2015, held in Santiage de Compostela, Spain, in June 2015.