Pattern recognition:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Amsterdam
Elsevier/Academic Press
©2006
|
| Ausgabe: | Third edition |
| Schlagworte: | |
| Online-Zugang: | FAW01 FAW02 Volltext |
| Beschreibung: | 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 |
| Beschreibung: | 1 Online-Ressource (xvi, 837 pages) Illustrationen, Diagramme |
| ISBN: | 0080513611 0123695317 9780080513614 9780123695314 |
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| 250 | |a Third edition | ||
| 264 | 1 | |a Amsterdam |b Elsevier/Academic Press |c ©2006 | |
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| 500 | |a Includes bibliographical references and index | ||
| 500 | |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 | ||
| 500 | |a A classic -- | ||
| 500 | |a 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. | ||
| 500 | |a 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. | ||
| 500 | |a 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 | ||
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| 650 | 7 | |a Pattern recognition systems |2 fast | |
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Datensatz im Suchindex
| _version_ | 1804175411366592512 |
|---|---|
| any_adam_object | |
| author | Theodoridis, Sergios |
| author_facet | Theodoridis, Sergios |
| author_role | aut |
| author_sort | Theodoridis, Sergios |
| author_variant | s t st |
| building | Verbundindex |
| bvnumber | BV043044976 |
| collection | ZDB-4-EBA |
| ctrlnum | (OCoLC)239612960 (DE-599)BVBBV043044976 |
| dewey-full | 006.4 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.4 |
| dewey-search | 006.4 |
| dewey-sort | 16.4 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| edition | Third edition |
| format | Electronic eBook |
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| id | DE-604.BV043044976 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T07:15:52Z |
| institution | BVB |
| isbn | 0080513611 0123695317 9780080513614 9780123695314 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-028469514 |
| oclc_num | 239612960 |
| open_access_boolean | |
| owner | DE-1046 DE-1047 |
| owner_facet | DE-1046 DE-1047 |
| physical | 1 Online-Ressource (xvi, 837 pages) Illustrationen, Diagramme |
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| publishDate | 2006 |
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| publisher | Elsevier/Academic Press |
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| spelling | Theodoridis, Sergios Verfasser aut Pattern recognition Sergios Theodoridis and Konstantinos Koutroumbas Third edition Amsterdam Elsevier/Academic Press ©2006 1 Online-Ressource (xvi, 837 pages) Illustrationen, Diagramme txt rdacontent c rdamedia cr rdacarrier 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 Pattern recognition systems Reconnaissance des formes (Informatique) COMPUTERS / Optical Data Processing bisacsh Pattern recognition systems fast Mustererkennung (DE-588)4040936-3 gnd rswk-swf MATLAB (DE-588)4329066-8 gnd rswk-swf Mustererkennung (DE-588)4040936-3 s MATLAB (DE-588)4329066-8 s 1\p DE-604 Koutroumbas, Konstantinos Sonstige oth http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=230861 Aggregator Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Theodoridis, Sergios Pattern recognition Pattern recognition systems Reconnaissance des formes (Informatique) COMPUTERS / Optical Data Processing bisacsh Pattern recognition systems fast Mustererkennung (DE-588)4040936-3 gnd MATLAB (DE-588)4329066-8 gnd |
| subject_GND | (DE-588)4040936-3 (DE-588)4329066-8 |
| title | Pattern recognition |
| title_auth | Pattern recognition |
| title_exact_search | Pattern recognition |
| title_full | Pattern recognition Sergios Theodoridis and Konstantinos Koutroumbas |
| title_fullStr | Pattern recognition Sergios Theodoridis and Konstantinos Koutroumbas |
| title_full_unstemmed | Pattern recognition Sergios Theodoridis and Konstantinos Koutroumbas |
| title_short | Pattern recognition |
| title_sort | pattern recognition |
| topic | Pattern recognition systems Reconnaissance des formes (Informatique) COMPUTERS / Optical Data Processing bisacsh Pattern recognition systems fast Mustererkennung (DE-588)4040936-3 gnd MATLAB (DE-588)4329066-8 gnd |
| topic_facet | Pattern recognition systems Reconnaissance des formes (Informatique) COMPUTERS / Optical Data Processing Mustererkennung MATLAB |
| url | http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=230861 |
| work_keys_str_mv | AT theodoridissergios patternrecognition AT koutroumbaskonstantinos patternrecognition |