Applied Pattern Recognition


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Applied Pattern Recognition


Applied Pattern Recognition

Author: Dietrich Paulus

language: en

Publisher: Springer Science & Business Media

Release Date: 2003-02-25


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This book demonstrates the efficiency of the C++ programming language in the realm of pattern recognition and pattern analysis. For this 4th edition, new features of the C++ language were integrated and their relevance for image and speech processing is discussed.

Applied Pattern Recognition


Applied Pattern Recognition

Author: Dietrich W.R. Paulus

language: en

Publisher: Morgan Kaufmann Publishers

Release Date: 1998


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This book demonstrates the efficiency of the C++ programming language in the realm of pattern recognition and pattern analysis. It introduces the basics of software engineering, image and speech processing, als well as fundamental mathematical tools for pattern recognition. Step by step the C++ programming language is discribed. Each step is illustrated by examples based on challenging problems in image und speech processing. Particular emphasis is put on object-oriented programming and the implementation of efficient algorithms. The book proposes a general class hierarchy for image segmentation. The essential parts of an implementation are presented. An object-oriented system for speech classification based on stochastic models is described.

A Probabilistic Theory of Pattern Recognition


A Probabilistic Theory of Pattern Recognition

Author: Luc Devroye

language: en

Publisher: Springer Science & Business Media

Release Date: 2013-11-27


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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, free 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.