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 **interes** at Nov. 28, 2015

English | 2015 | ISBN: 1482251884 | 392 pages | PDF | 10 MB

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

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

Posted by **tot167** at Dec. 9, 2010

The M.I.T Press | 2001 | ISBN: 026208306X | 384 pages | PDF | 2,4 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 **Alexpal** at Jan. 22, 2007

Publisher: The MIT Press (August 1, 2001) | ISBN-10: 026208290X | PDF | 30,6 Mb | 425 pages

The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner.

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

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