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A Probabilistic Theory of Pattern Recognition (Stochastic Modelling and Applied Probability, 31)
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A Probabilistic Theory Of Pattern Recognition, Sie (Pb-2014)
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A Probabilistic Theory of Pattern Recognition (Stochastic Modelling and Applied Probability) by Luc Devroye (1997-02-20)
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Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)
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A First Course in Machine Learning (Chapman & Hall/Crc Machine Learning & Pattern Recognition)
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Specifications Suggested Age: 22 Years and Up Number of Pages: 638 Genre: Mathematics Sub-Genre: Probability \u0026 Statistics Series Title: Stochastic Modelling and Applied Probability Format: Paperback Publisher: Springer Book theme: General Author: Luc Devroye \u0026 Laszlo Györfi \u0026 Gabor Lugosi Language: English Street Date : November 22, 2013 TCIN : 85052037 UPC : 9781461268772 Item Number (DPCI) : 247-43-7462 Origin : Made in the USA or Imported Description Book Synopsis A self-contained and coherent account of probabilistic techniques, covering: distance measures, kernel rules, nearest neighbour rules, Vapnik-Chervonenkis theory, parametric classification, and feature extraction. Each chapter concludes with problems and exercises to further the readers understanding. Both research workers and graduate students will benefit from this wide-ranging and up-to-date account of a fast- moving field. If the item details above aren’t accurate or complete, we want to know about it. Report incorrect product info.
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A self-contained and coherent account of probabilistic techniques, covering: distance measures, kernel rules, nearest neighbour rules, Vapnik-Chervonenkis theory, parametric classification, and feature extraction. Each chapter concludes with problems and exercises to further the readers understanding. Both research workers and graduate students will benefit from this wide-ranging and up-to-date account of a fast- moving field. • Author: Luc Devroye,Laszlo Gy\\u0026ouml;rfi,Gabor Lugosi • ISBN:9780387946184 • Format:Hardcover • Publication Date:1997-02-20
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A self-contained and coherent account of probabilistic techniques, covering: distance measures, kernel rules, nearest neighbour rules, Vapnik-Chervonenkis theory, parametric classification, and feature extraction. Each chapter concludes with problems and exercises to further the readers understanding. Both research workers and graduate students will benefit from this wide-ranging and up-to-date account of a fast- moving field. • Author: Luc Devroye,Laszlo Gy\\u0026ouml;rfi,Gabor Lugosi • ISBN:9781461268772 • Format:Paperback • Publication Date:2013-11-22
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Specifications Suggested Age: 22 Years and Up Number of Pages: 638 Genre: Mathematics Sub-Genre: Probability \u0026 Statistics Series Title: Stochastic Modelling and Applied Probability Format: Hardcover Publisher: Springer Book theme: General Author: Luc Devroye \u0026 Laszlo Györfi \u0026 Gabor Lugosi Language: English Street Date : February 20, 1997 TCIN : 85836809 UPC : 9780387946184 Item Number (DPCI) : 247-14-4048 Origin : Made in the USA or Imported Description Book Synopsis A self-contained and coherent account of probabilistic techniques, covering: distance measures, kernel rules, nearest neighbour rules, Vapnik-Chervonenkis theory, parametric classification, and feature extract
ion. Each chapter concludes with problems and exercises to further the readers understanding. Both research workers and graduate students will benefit from this wide-ranging and up-to-date account of a fast- moving field. From the Back Cover Pattern recognition presents one of the most significant challenges for scientists and engineers, and many different approaches have been proposed. The aim of this book is to provide a self-contained account of probabilistic analysis of these approaches. The book includes a discussion of distance measures, nonparametric methods based on kernels or nearest neighbors, Vapnik-Chervonenkis theory, epsilon entropy, parametric classification, error estimation, tree classifiers, and neural networks. Wherever possible, distribution-free properties and inequalities are derived. A substantial portion of the results or the analysis is new. Over 430 problems and exercises complement the material. If the item details above aren’t accurate or complete, we want to know about it. Report incorrect product info.
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Proceedings of the NATO Advanced Study Institute on Pattern Recognition Theory and Applications held in Spa-Balmoral, Belgium, June 9-20, 1986 This book is the outcome of a NATO Advanced Study Institute on Pattern Recog- nition Theory and Applications held in Spa-Balmoral, Belgium, in June 1986. This Institute was the third of a series which started in 1975 in Bandol, France, at the initia- tive of Professors K. S. Fu and A. Whinston, and continued in 1981 in Oxford, UK, with Professors K. S. Fu, J. Kittler and L. -F. Pau as directors. As early as in 1981, plans were made to pursue the series in about 1986 and possibly in Belgium, with Professor K. S. Fu and the present editors as directors. Unfortunately, Ie sort en decida autrement: Professor Fu passed away in the spring of 1985. His sudden death was an irreparable loss to the scientific community and to all those who knew him as an inspiring colleague, a teacher or a dear friend. Soon after, Josef Kittler and I decided to pay a small tribute to his memory by helping some of his plans to materialize. With the support of the NATO Scientific Affairs Division, the Institute became a reality. It was therefore but natural that the proceedings of the Institute be dedicated to him. The book contains most of the papers that were presented at the Institute. Papers are grouped along major themes which hopefully represent the major areas of contem- porary research. These are: 1. Statistical methods and clustering techniques 2. Probabilistic relaxation techniques 3. From Markovian to connectionist models 4. • ISBN:9783642830716 • Format:Paperback • Publication Date:2012-03-01
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Pattern Recognition Algorithms for Data Mining addresses different pattern recognition (PR) tasks in a unified framework with both theoretical and experimental results. Tasks covered include data condensation, feature selection, case generation, clustering/classification, and rule generation and evaluation. This volume presents various theories, methodologies, and algorithms, using both classical approaches and hybrid paradigms. The authors emphasize large datasets with overlapping, intractable, or nonlinear boundary classes, and datasets that demonstrate granular computing in soft frameworks. Organized into eight chapters, the book begins with an introduction to PR, data mining, and knowledge discovery concepts. The authors analyze the tasks of multi-scale data condensation and dimensionality reduction, then explore the problem of learning with support vector machine (SVM). They conclude by highlighting the significance of granular computing for different mining tasks in a soft paradigm. • Author: Sankar K Pal,Pabitra Mitra,Pal K Pal • ISBN:9781584884576 • Format:Hardcover • Publication Date:2004-05-27
