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Pattern recognition

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Pattern recognition/ Sergios Theodoridis and Konstantinos Koutroumbas.
作者:	Theodoridis, Sergios,
其他作者:	Koutroumbas, Konstantinos,
出版者:	Amsterdam ;Elsevier/Academic Press, : c2006.,
面頁冊數:	xvi, 837 p. :ill. ; : 24 cm.;
標題:	Pattern recognition systems. -
電子資源:	An electronic book accessible through the World Wide Web; click for information
電子資源:	An electronic book accessible through the World Wide Web; click for information
ISBN:	9780080513614 (electronic bk.)

Pattern recognition
Theodoridis, Sergios,1951-

Pattern recognition[electronic resource] /Sergios Theodoridis and Konstantinos Koutroumbas. - 3rd ed. - Amsterdam ;Elsevier/Academic Press,c2006. - xvi, 837 p. :ill. ;24 cm.

Includes bibliographical references and index.

Chapter 1: Introduction -- Chapter 2: Classifiers Based on Bayes Decision Theory -- Chapter 3: Linear Classifiers -- Chapter 4: Nonlinear Classifiers -- Chapter 5: Feature Selection -- Chapter 6: Feature Generation I -- Chapter 7: Feature Generation II -- Chapter 8: Template Matching -- Chapter 9: Context-Dependant Classification -- Chapter 10: System Evaluation -- Chapter 11: Clustering: Basic Concepts -- Chapter 12: Clustering Algorithms I (Sequential) -- Chapter 13: Clustering Algorithms II (Hierarchical) -- Chapter 14: Clustering Algorithms III (Functional Optimization) -- Chapter 15: Clustering Algorithms IV (Graph Theory) -- Chapter 16: Cluster Validity.

A classic -- offering comprehensive and unified coverage with a balance between theory and practice! Pattern recognition is integral to a wide spectrum of scientific disciplines and technologies including image analysis, speech recognition, audio classification, communications, computer-aided diagnosis, and data mining. The authors, leading experts in the field of pattern recognition, have once again provided an up-to-date, self-contained volume encapsulating this wide spectrum of information. Each chapter is designed to begin with basics of theory progressing to advanced topics and then discusses cutting-edge techniques. Problems and exercises are present at the end of each chapter with a solutions manual provided via a companion website where a number of demonstrations are also available to aid the reader in gaining practical experience with the theories and associated algorithms. This edition includes discussion of Bayesian classification, Bayesian networks, linear and nonlinear classifier design (including neural networks and support vector machines), dynamic programming and hidden Markov models for sequential data, feature generation (including wavelets, principal component analysis, independent component analysis and fractals), feature selection techniques, basic concepts from learning theory, and clustering concepts and algorithms. This book considers classical and current theory and practice, of both supervised and unsupervised pattern recognition, to build a complete background for professionals and students of engineering. FOR INSTRUCTORS: To obtain access to the solutions manual for this title simply register on our textbook website (textbooks.elsevier.com)and request access to the Computer Science or Electronics and Electrical Engineering subject area. Once approved (usually within one business day) you will be able to access all of the instructor-only materials through the "Instructor Manual" link on this book's full web page. * The latest results on support vector machines including v-SVM's and their geometric interpretation * Classifier combinations including the Boosting approach * State-of-the-art material for clustering algorithms tailored for large data sets and/or high dimensional data, as required by applications such as web-mining and bioinformatics * Coverage of diverse applications such as image analysis, optical character recognition, channel equalization, speech recognition and audio classification.

Electronic reproduction.
Amsterdam :
Elsevier Science & Technology,
2007.

Mode of access: World Wide Web.

ISBN: 9780080513614 (electronic bk.)

Source: 116089:116187Elsevier Science & Technologyhttp://www.sciencedirect.comSubjects--Topical Terms:

557384
Pattern recognition systems.
Index Terms--Genre/Form:

554714
Electronic books.

LC Class. No.: TK7882.P3 / T46 2006eb

Dewey Class. No.: 006.4

Pattern recognition
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003 OCoLC
005 20090923090927.0
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007 cr cn
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020 $a 0080513611 (electronic bk.)
020 $a 9780123695314 (paper)
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100 1 $a Theodoridis, Sergios, $d 1951- $3 636605
245 1 0 $a Pattern recognition $h [electronic resource] / $c Sergios Theodoridis and Konstantinos Koutroumbas.
250 $a 3rd ed.
260 $a Amsterdam ; $a Boston : $c c2006. $b Elsevier/Academic Press,
300 $a xvi, 837 p. : $b ill. ; $c 24 cm.
504 $a Includes bibliographical references and index.
505 0 $a Chapter 1: Introduction -- Chapter 2: Classifiers Based on Bayes Decision Theory -- Chapter 3: Linear Classifiers -- Chapter 4: Nonlinear Classifiers -- Chapter 5: Feature Selection -- Chapter 6: Feature Generation I -- Chapter 7: Feature Generation II -- Chapter 8: Template Matching -- Chapter 9: Context-Dependant Classification -- Chapter 10: System Evaluation -- Chapter 11: Clustering: Basic Concepts -- Chapter 12: Clustering Algorithms I (Sequential) -- Chapter 13: Clustering Algorithms II (Hierarchical) -- Chapter 14: Clustering Algorithms III (Functional Optimization) -- Chapter 15: Clustering Algorithms IV (Graph Theory) -- Chapter 16: Cluster Validity.
520 $a A classic -- offering comprehensive and unified coverage with a balance between theory and practice! Pattern recognition is integral to a wide spectrum of scientific disciplines and technologies including image analysis, speech recognition, audio classification, communications, computer-aided diagnosis, and data mining. The authors, leading experts in the field of pattern recognition, have once again provided an up-to-date, self-contained volume encapsulating this wide spectrum of information. Each chapter is designed to begin with basics of theory progressing to advanced topics and then discusses cutting-edge techniques. Problems and exercises are present at the end of each chapter with a solutions manual provided via a companion website where a number of demonstrations are also available to aid the reader in gaining practical experience with the theories and associated algorithms. This edition includes discussion of Bayesian classification, Bayesian networks, linear and nonlinear classifier design (including neural networks and support vector machines), dynamic programming and hidden Markov models for sequential data, feature generation (including wavelets, principal component analysis, independent component analysis and fractals), feature selection techniques, basic concepts from learning theory, and clustering concepts and algorithms. This book considers classical and current theory and practice, of both supervised and unsupervised pattern recognition, to build a complete background for professionals and students of engineering. FOR INSTRUCTORS: To obtain access to the solutions manual for this title simply register on our textbook website (textbooks.elsevier.com)and request access to the Computer Science or Electronics and Electrical Engineering subject area. Once approved (usually within one business day) you will be able to access all of the instructor-only materials through the "Instructor Manual" link on this book's full web page. * The latest results on support vector machines including v-SVM's and their geometric interpretation * Classifier combinations including the Boosting approach * State-of-the-art material for clustering algorithms tailored for large data sets and/or high dimensional data, as required by applications such as web-mining and bioinformatics * Coverage of diverse applications such as image analysis, optical character recognition, channel equalization, speech recognition and audio classification.
533 $a Electronic reproduction. $b Amsterdam : $c Elsevier Science & Technology, $d 2007. $n Mode of access: World Wide Web. $n System requirements: Web browser. $n Title from title screen (viewed on July 25, 2007). $n Access may be restricted to users at subscribing institutions.
650 0 $a Pattern recognition systems. $3 557384
655 7 $a Electronic books. $2 local $3 554714
700 1 $a Koutroumbas, Konstantinos, $d 1967- $3 636606
710 2 $a ScienceDirect (Online service) $3 636041
856 4 0 $3 ScienceDirect $u http://www.sciencedirect.com/science/book/9780123695314 $z An electronic book accessible through the World Wide Web; click for information
856 4 0 $3 Referex $u http://www.engineeringvillage.com/controller/servlet/OpenURL?genre=book&isbn=9780123695314 $z An electronic book accessible through the World Wide Web; click for information