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 **naag** at March 29, 2017

2014 | 232 Pages | ISBN: 1482226669 | PDF | 2 MB

Posted by **naag** at March 28, 2017

2014 | 238 Pages | ISBN: 1783988363 | EPUB | 6.20 MB

Posted by **thingska** at March 26, 2017

English | 2017 | ISBN: 1491962291 | 566 Pages | EPUB | 8.41 MB

Posted by **AvaxGenius** at March 26, 2017

English | PDF | 2015 | 760 Pages | ISBN : 3319235281 | 26.78 MB

The three volume set LNAI 9284, 9285, and 9286 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2015, held in Porto, Portugal, in September 2015.

Posted by **AvaxGenius** at March 25, 2017

English | PDF,EPUB | 2015 | 142 Pages | ISBN : 9812874674 | 4.84 MB

The book encompasses the state-of-the-art visual quality assessment (VQA) and learning based visual quality assessment (LB-VQA) by providing a comprehensive overview of the existing relevant methods.

Posted by **naag** at March 24, 2017

2017 | English | ASIN: B06XT6M64L | 210 pages | PDF + EPUB (conv) | 0.7 Mb

Posted by **readerXXI** at March 23, 2017

English | 2016 | ISBN: 1785888404 | 358 Pages | PDF | 6.34 MB

This book is targeted at .NET developers who want to build complex machine learning systems. Some basic understanding of data science is required.