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_aQ325.5 _b.H38 2009 |
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_a005.1 _222 |
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_a003.5 _222 |
| 049 | _aMAIN | ||
| 100 | 1 | _aHamel, Lutz. | |
| 245 | 1 | 0 |
_aKnowledge discovery with support vector machines / _cLutz Hamel. |
| 260 |
_aHoboken, N.J. : _bWiley, _c�2009. |
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| 300 |
_a1 online resource (xv, 246 pages) : _billustrations |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 504 | _aIncludes bibliographical references (pages 231-235) and index. | ||
| 505 | 0 | _aKNOWLEDGE DISCOVERY WITH SUPPORT VECTOR MACHINES; CONTENTS; PREFACE; PART I; 1 WHAT IS KNOWLEDGE DISCOVERY?; 2 KNOWLEDGE DISCOVERY ENVIRONMENTS; 3 DESCRIBING DATA MATHEMATICALLY; 4 LINEAR DECISION SURFACES AND FUNCTIONS; 5 PERCEPTRON LEARNING; 6 MAXIMUM-MARGIN CLASSIFIERS; PART II; 7 SUPPORT VECTOR MACHINES; 8 IMPLEMENTATION; 9 EVALUATING WHAT HAS BEEN LEARNED; 10 ELEMENTS OF STATISTICAL LEARNING THEORY; PART III; 11 MULTICLASS CLASSIFICATION; 12 REGRESSION WITH SUPPORT VECTOR MACHINES; 13 NOVELTY DETECTION; APPENDIX A NOTATION; APPENDIX B TUTORIAL INTRODUCTION TO R. | |
| 520 | _aAn easy-to-follow introduction to support vector machines. This book provides an in-depth, easy-to-follow introduction to support vector machines drawing only from minimal, carefully motivated technical and mathematical background material. It begins with a cohesive discussion of machine learning and goes on to cover:. Knowledge discovery environments;. Describing data mathematically;. Linear decision surfaces and functions;. Perceptron learning;. Maximum margin classifiers;. Support vector machines;. Elements of statistical learning theory;. Multi-class classification;. Regression with supporsupport vector machines;. Novelty detection. Complemented with hands-on exercises, algorithm descriptions, and data sets, Knowledge Discovery with Support Vector Machines is an invaluable textbook for advanced undergraduate and graduate courses. It is also an excellent tutorial on support vector machines for professionals who are pursuing research in machine learning and related areas. | ||
| 588 | 0 | _aPrint version record. | |
| 590 |
_aJohn Wiley and Sons _bWiley Online Library: Complete oBooks |
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| 650 | 0 | _aSupport vector machines. | |
| 650 | 0 | _aData mining. | |
| 650 | 0 | _aMachine learning. | |
| 650 | 0 | _aComputer algorithms. | |
| 650 | 0 | _aAlgorithms. | |
| 650 | 6 | _aMachines �a vecteurs supports. | |
| 650 | 6 | _aExploration de donn�ees (Informatique) | |
| 650 | 6 | _aApprentissage automatique. | |
| 650 | 6 | _aAlgorithmes. | |
| 650 | 7 |
_aalgorithms. _2aat |
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| 650 | 7 |
_aCOMPUTERS _xCybernetics. _2bisacsh |
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| 650 | 7 |
_aCOMPUTERS _xProgramming _xOpen Source. _2bisacsh |
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| 650 | 7 |
_aCOMPUTERS _xSoftware Development & Engineering _xTools. _2bisacsh |
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| 650 | 7 |
_aCOMPUTERS _xSoftware Development & Engineering _xGeneral. _2bisacsh |
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| 650 | 7 |
_aAlgorithms _2fast |
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| 650 | 7 |
_aComputer algorithms _2fast |
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| 650 | 7 |
_aData mining _2fast |
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| 650 | 7 |
_aMachine learning _2fast |
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| 650 | 7 |
_aSupport vector machines _2fast |
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| 758 |
_ihas work: _aKnowledge discovery with support vector machines (Text) _1https://id.oclc.org/worldcat/entity/E39PCGhwY38Tp6xdwVjfvcpjJC _4https://id.oclc.org/worldcat/ontology/hasWork |
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| 776 | 0 | 8 |
_iPrint version: _aHamel, Lutz. _tKnowledge discovery with support vector machines. _dHoboken, N.J. : John Wiley & Sons, �2009 _z9780470371923 _w(DLC) 2009011948 _w(OCoLC)294883424 |
| 856 | 4 | 0 | _uhttps://onlinelibrary.wiley.com/doi/book/10.1002/9780470503065 |
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