Knowledge discovery with support vector machines / Lutz Hamel.
Material type:
TextPublication details: Hoboken, N.J. : Wiley, �2009.Description: 1 online resource (xv, 246 pages) : illustrationsContent type: - text
- computer
- online resource
- 9780470503041
- 0470503041
- 9780470503065
- 0470503068
- Support vector machines
- Data mining
- Machine learning
- Computer algorithms
- Algorithms
- Machines �a vecteurs supports
- Exploration de donn�ees (Informatique)
- Apprentissage automatique
- Algorithmes
- algorithms
- COMPUTERS -- Cybernetics
- COMPUTERS -- Programming -- Open Source
- COMPUTERS -- Software Development & Engineering -- Tools
- COMPUTERS -- Software Development & Engineering -- General
- Algorithms
- Computer algorithms
- Data mining
- Machine learning
- Support vector machines
- 005.1 22
- 003.5 22
- Q325.5 .H38 2009
Includes bibliographical references (pages 231-235) and index.
KNOWLEDGE 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.
An 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.
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