Pattern recognition /:
This book considers classical and current theory and practice, of supervised, unsupervised and semi-supervised pattern recognition, to build a complete background for professionals and students of engineering. The authors, leading experts in the field of pattern recognition, have provided an up-to-d...
Gespeichert in:
| 1. Verfasser: | |
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| Weitere Verfasser: | |
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Burlington, MA ; London :
Academic Press,
©2009.
|
| Ausgabe: | 4th ed. |
| Schlagworte: | |
| Online-Zugang: | Volltext Volltext |
| Zusammenfassung: | This book considers classical and current theory and practice, of supervised, unsupervised and semi-supervised pattern recognition, to build a complete background for professionals and students of engineering. The authors, leading experts in the field of pattern recognition, have provided an up-to-date, self-contained volume encapsulating this wide spectrum of information. The very latest methods are incorporated in this edition: semi-supervised learning, combining clustering algorithms, and relevance feedback. Thoroughly developed to include many more worked examples to give greater understanding of the various methods and techniques Many more diagrams included--now in two color--to provide greater insight through visual presentation Matlab code of the most common methods are given at the end of each chapter An accompanying book with 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. The companion book is available separately or at a special packaged price (Book ISBN: 9780123744869. Package ISBN: 9780123744913) Latest hot topics included to further the reference value of the text including non-linear dimensionality reduction techniques, relevance feedback, semi-supervised learning, spectral clustering, combining clustering algorithms Solutions manual, powerpoint slides, and additional resources are available to faculty using the text for their course. Register at www.textbooks.elsevier.com and search on "Theodoridis" to access resources for instructor. |
| Beschreibung: | 1 online resource (xvii, 961 pages) : illustrations |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9781597492720 1597492728 9780080949123 0080949126 |
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| 520 | |a This book considers classical and current theory and practice, of supervised, unsupervised and semi-supervised pattern recognition, to build a complete background for professionals and students of engineering. The authors, leading experts in the field of pattern recognition, have provided an up-to-date, self-contained volume encapsulating this wide spectrum of information. The very latest methods are incorporated in this edition: semi-supervised learning, combining clustering algorithms, and relevance feedback. Thoroughly developed to include many more worked examples to give greater understanding of the various methods and techniques Many more diagrams included--now in two color--to provide greater insight through visual presentation Matlab code of the most common methods are given at the end of each chapter An accompanying book with 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. The companion book is available separately or at a special packaged price (Book ISBN: 9780123744869. Package ISBN: 9780123744913) Latest hot topics included to further the reference value of the text including non-linear dimensionality reduction techniques, relevance feedback, semi-supervised learning, spectral clustering, combining clustering algorithms Solutions manual, powerpoint slides, and additional resources are available to faculty using the text for their course. Register at www.textbooks.elsevier.com and search on "Theodoridis" to access resources for instructor. | ||
| 505 | 0 | |a 1. Introduction -- 2. Classifiers based on Bayes Decision -- 3. Linear Classifiers -- 4. Nonlinear Classifiers -- 5. Feature Selection -- 6. Feature Generation I: Data Transformation and Dimensionality Reduction -- 7. Feature Generation II -- 8. Template Matching -- 9. Context Depedant Clarification -- 10. System Evaultion -- 11. Clustering: Basic Concepts -- 12. Clustering Algorithms: Algorithms L Sequential -- 13. Clustering Algorithms II: Hierarchical -- 14. Clustering Algorithms III: Based on Function Optimization -- 15. Clustering Algorithms IV: Clustering -- 16. Cluster Validity. | |
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a Classifiers based on Bayes Decision Theory -- Linear classifiers -- Nonlinear classifiers -- Feature selection -- Feature generation I : data transformation and dimensionality reduction -- Feature generation II -- Template matching -- Context-dependent classification -- Supervised learning : the epilogue -- Clustering algorithms I : sequential algorithms -- Clustering algorithms II : hierarchial algorithms -- Clustering algorithms III : schemes based on function optimization -- Clustering algorithms IV -- Cluster validity. | |
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| adam_text | |
| any_adam_object | |
| author | Theodoridis, Sergios, 1951- |
| author2 | Koutroumbas, Konstantinos, 1967- |
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| contents | 1. Introduction -- 2. Classifiers based on Bayes Decision -- 3. Linear Classifiers -- 4. Nonlinear Classifiers -- 5. Feature Selection -- 6. Feature Generation I: Data Transformation and Dimensionality Reduction -- 7. Feature Generation II -- 8. Template Matching -- 9. Context Depedant Clarification -- 10. System Evaultion -- 11. Clustering: Basic Concepts -- 12. Clustering Algorithms: Algorithms L Sequential -- 13. Clustering Algorithms II: Hierarchical -- 14. Clustering Algorithms III: Based on Function Optimization -- 15. Clustering Algorithms IV: Clustering -- 16. Cluster Validity. Classifiers based on Bayes Decision Theory -- Linear classifiers -- Nonlinear classifiers -- Feature selection -- Feature generation I : data transformation and dimensionality reduction -- Feature generation II -- Template matching -- Context-dependent classification -- Supervised learning : the epilogue -- Clustering algorithms I : sequential algorithms -- Clustering algorithms II : hierarchial algorithms -- Clustering algorithms III : schemes based on function optimization -- Clustering algorithms IV -- Cluster validity. |
| ctrlnum | (OCoLC)610009838 |
| dewey-full | 006.4 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
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| dewey-search | 006.4 |
| dewey-sort | 16.4 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| edition | 4th ed. |
| format | Electronic eBook |
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| id | ZDB-4-EBA-ocn610009838 |
| illustrated | Illustrated |
| indexdate | 2024-11-27T13:17:10Z |
| institution | BVB |
| isbn | 9781597492720 1597492728 9780080949123 0080949126 |
| language | English |
| oclc_num | 610009838 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (xvii, 961 pages) : illustrations |
| psigel | ZDB-4-EBA |
| publishDate | 2009 |
| publishDateSearch | 2009 |
| publishDateSort | 2009 |
| publisher | Academic Press, |
| record_format | marc |
| spelling | Theodoridis, Sergios, 1951- https://id.oclc.org/worldcat/entity/E39PCjxcjmqG9c6pj4TqgXMGjK http://id.loc.gov/authorities/names/n98061001 Pattern recognition / Sergios Theodoridis, Konstantinos Koutroumbas. 4th ed. Burlington, MA ; London : Academic Press, ©2009. 1 online resource (xvii, 961 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier data file rda This book considers classical and current theory and practice, of supervised, unsupervised and semi-supervised pattern recognition, to build a complete background for professionals and students of engineering. The authors, leading experts in the field of pattern recognition, have provided an up-to-date, self-contained volume encapsulating this wide spectrum of information. The very latest methods are incorporated in this edition: semi-supervised learning, combining clustering algorithms, and relevance feedback. Thoroughly developed to include many more worked examples to give greater understanding of the various methods and techniques Many more diagrams included--now in two color--to provide greater insight through visual presentation Matlab code of the most common methods are given at the end of each chapter An accompanying book with 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. The companion book is available separately or at a special packaged price (Book ISBN: 9780123744869. Package ISBN: 9780123744913) Latest hot topics included to further the reference value of the text including non-linear dimensionality reduction techniques, relevance feedback, semi-supervised learning, spectral clustering, combining clustering algorithms Solutions manual, powerpoint slides, and additional resources are available to faculty using the text for their course. Register at www.textbooks.elsevier.com and search on "Theodoridis" to access resources for instructor. 1. Introduction -- 2. Classifiers based on Bayes Decision -- 3. Linear Classifiers -- 4. Nonlinear Classifiers -- 5. Feature Selection -- 6. Feature Generation I: Data Transformation and Dimensionality Reduction -- 7. Feature Generation II -- 8. Template Matching -- 9. Context Depedant Clarification -- 10. System Evaultion -- 11. Clustering: Basic Concepts -- 12. Clustering Algorithms: Algorithms L Sequential -- 13. Clustering Algorithms II: Hierarchical -- 14. Clustering Algorithms III: Based on Function Optimization -- 15. Clustering Algorithms IV: Clustering -- 16. Cluster Validity. Includes bibliographical references and index. Classifiers based on Bayes Decision Theory -- Linear classifiers -- Nonlinear classifiers -- Feature selection -- Feature generation I : data transformation and dimensionality reduction -- Feature generation II -- Template matching -- Context-dependent classification -- Supervised learning : the epilogue -- Clustering algorithms I : sequential algorithms -- Clustering algorithms II : hierarchial algorithms -- Clustering algorithms III : schemes based on function optimization -- Clustering algorithms IV -- Cluster validity. Print version record. Pattern recognition systems. http://id.loc.gov/authorities/subjects/sh85098791 Pattern Recognition, Automated https://id.nlm.nih.gov/mesh/D010363 Reconnaissance des formes (Informatique) COMPUTERS Optical Data Processing. bisacsh Pattern recognition systems fast Online-Ressource gnd Patroonherkenning. gtt Koutroumbas, Konstantinos, 1967- https://id.oclc.org/worldcat/entity/E39PCjxJr3Qg8K6q7r9m76YcCP http://id.loc.gov/authorities/names/n98061012 Print version: Theodoridis, Sergios, 1951- Pattern recognition. 4th ed. Burlington, MA ; London : Academic Press, ©2009 9781597492720 1597492728 (OCoLC)244653634 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=320843 Volltext FWS01 ZDB-4-EBA FWS_PDA_EBA https://www.sciencedirect.com/science/book/9781597492720 Volltext |
| spellingShingle | Theodoridis, Sergios, 1951- Pattern recognition / 1. Introduction -- 2. Classifiers based on Bayes Decision -- 3. Linear Classifiers -- 4. Nonlinear Classifiers -- 5. Feature Selection -- 6. Feature Generation I: Data Transformation and Dimensionality Reduction -- 7. Feature Generation II -- 8. Template Matching -- 9. Context Depedant Clarification -- 10. System Evaultion -- 11. Clustering: Basic Concepts -- 12. Clustering Algorithms: Algorithms L Sequential -- 13. Clustering Algorithms II: Hierarchical -- 14. Clustering Algorithms III: Based on Function Optimization -- 15. Clustering Algorithms IV: Clustering -- 16. Cluster Validity. Classifiers based on Bayes Decision Theory -- Linear classifiers -- Nonlinear classifiers -- Feature selection -- Feature generation I : data transformation and dimensionality reduction -- Feature generation II -- Template matching -- Context-dependent classification -- Supervised learning : the epilogue -- Clustering algorithms I : sequential algorithms -- Clustering algorithms II : hierarchial algorithms -- Clustering algorithms III : schemes based on function optimization -- Clustering algorithms IV -- Cluster validity. Pattern recognition systems. http://id.loc.gov/authorities/subjects/sh85098791 Pattern Recognition, Automated https://id.nlm.nih.gov/mesh/D010363 Reconnaissance des formes (Informatique) COMPUTERS Optical Data Processing. bisacsh Pattern recognition systems fast Online-Ressource gnd Patroonherkenning. gtt |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85098791 https://id.nlm.nih.gov/mesh/D010363 |
| title | Pattern recognition / |
| title_auth | Pattern recognition / |
| title_exact_search | Pattern recognition / |
| title_full | Pattern recognition / Sergios Theodoridis, Konstantinos Koutroumbas. |
| title_fullStr | Pattern recognition / Sergios Theodoridis, Konstantinos Koutroumbas. |
| title_full_unstemmed | Pattern recognition / Sergios Theodoridis, Konstantinos Koutroumbas. |
| title_short | Pattern recognition / |
| title_sort | pattern recognition |
| topic | Pattern recognition systems. http://id.loc.gov/authorities/subjects/sh85098791 Pattern Recognition, Automated https://id.nlm.nih.gov/mesh/D010363 Reconnaissance des formes (Informatique) COMPUTERS Optical Data Processing. bisacsh Pattern recognition systems fast Online-Ressource gnd Patroonherkenning. gtt |
| topic_facet | Pattern recognition systems. Pattern Recognition, Automated Reconnaissance des formes (Informatique) COMPUTERS Optical Data Processing. Pattern recognition systems Online-Ressource Patroonherkenning. |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=320843 https://www.sciencedirect.com/science/book/9781597492720 |
| work_keys_str_mv | AT theodoridissergios patternrecognition AT koutroumbaskonstantinos patternrecognition |