Pattern Recognition and Machine Learning, Christopher M. Bishop

Автор: Barber
Название: Bayesian Reasoning and Machine Learning
ISBN: 0521518148 ISBN-13(EAN): 9780521518147
Издательство: Cambridge Academ
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Цена: 11088.00 р.
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Описание: This practical introduction for final-year undergraduate and graduate students is ideally suited to computer scientists without a background in calculus and linear algebra. Numerous examples and exercises are provided. Additional resources available online and in the comprehensive software package include computer code, demos and teaching materials for instructors.

Автор: Sergios Theodoridis
Название: Pattern Recognition,
ISBN: 1597492728 ISBN-13(EAN): 9781597492720
Издательство: Elsevier Science
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Цена: 14483.00 р.
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Описание: Considers classical and theory and practice, of supervised, unsupervised and semi-supervised pattern recognition, to build a complete background for professionals and students of engineering. This book provides an self-contained volume encapsulating this spectrum of information.

Автор: Brian D. Ripley
Название: Pattern Recognition and Neural Networks
ISBN: 0521717701 ISBN-13(EAN): 9780521717700
Издательство: Cambridge Academ
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Цена: 7762.00 р.
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Описание: This 1996 book is a reliable account of the statistical framework for pattern recognition and machine learning. Valuable advice is included on both theory and applications, while case studies based on real data sets help readers develop their understanding. All data sets are available from www.stats.ox.ac.uk/~ripley/PRbook/

Автор: Sergios Theodoridis
Название: Introduction to Pattern Recognition: A Matlab Approach,
ISBN: 0123744865 ISBN-13(EAN): 9780123744869
Издательство: Elsevier Science
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Цена: 5557.00 р.
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Описание: An accompanying manual to "Theodoridis/Koutroumbas, Pattern Recognition", that includes Matlab code of the most common methods and algorithms in the book, together with a descriptive summary and solved examples, and including real-life data sets in imaging and audio recognition.

Автор: Kumar
Название: Correlation Pattern Recognition
ISBN: 0521153484 ISBN-13(EAN): 9780521153485
Издательство: Cambridge Academ
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Описание: This book provides a needed review of the diverse background material needed for correlation pattern recognition, developing the signal processing theory, the pattern recognition metrics, and the practical application know-how from basic premises. Includes case studies of interest, such as face and fingerprint recognition. For graduate students and practitioners.

Автор: Bishop, Christopher M.
Название: Neural Networks for Pattern Recognition
ISBN: 0198538642 ISBN-13(EAN): 9780198538646
Издательство: Oxford Academ
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Цена: 13939.00 р.
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Описание: This book is the first to provide a comprehensive account of neural networks from a statistical perspective. Its emphasis is on pattern recognition, which currently represents the area of greatest applicability for neural networks. By focusing on pattern recognition, the book provides a much more extensive treatment of many topics than is available in earlier books.

Автор: Mitchell
Название: Machine Learning
ISBN: 0071154671 ISBN-13(EAN): 9780071154673
Издательство: McGraw-Hill
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Цена: 10466.00 р.
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Описание: Covers the field of machine learning, which is the study of algorithms that allow computer programs to automatically improve through experience. This book is intended to support upper level undergraduate and introductory level graduate courses in machine learning.

Автор: Ashish Ghosh; Rajat K. De; Sankar K Pal
Название: Pattern Recognition and Machine Intelligence
ISBN: 3540770453 ISBN-13(EAN): 9783540770459
Издательство: Springer
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Цена: 16070.00 р.
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Описание: Constitutes the refereed proceedings of the Second International Conference on Pattern Recognition and Machine Intelligence, PReMI 2007, held in Kolkata, India in December 2007. This book presents 82 revised papers that were reviewed and selected from 241 submissions.

Автор: Bekkerman
Название: Scaling up Machine Learning
ISBN: 0521192242 ISBN-13(EAN): 9780521192248
Издательство: Cambridge Academ
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Описание: In many practical situations it is impossible to run existing machine learning methods on a single computer, because either the data is too large or the speed and throughput requirements are too demanding. Researchers and practitioners will find here a variety of machine learning methods developed specifically for parallel or distributed systems, covering algorithms, platforms and applications.