An introduction to support vector machines: and other kernel-based learning methods
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| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Cambridge [u.a.]
Cambridge Univ. Press
2003
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| Edition: | Repr. |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XIII, 189 S. graph. Darst. |
| ISBN: | 0521780195 |
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Record in the Search Index
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Titel: An introduction to support vector machines
Autor: Cristianini, Nello
Jahr: 2003
An Introduction to Support Vector Machines
and other kernel-based learning methods
NELLO CRISTIANINI AND JOHN SHAWE-TAYLOR
CAMBRIDGE
UNIVERSITY PRESS
Contents
Preface ix
Notation xiii
1 The Learning Methodology 1
1.1 Supervised Learning 1
1.2 Learning and Generalisation 3
1.3 Improving Generalisation 4
1.4 Attractions and Drawbacks of Learning 6
1.5 Support Vector Machines for Learning 7
1.6 Exercises 7
1.7 Further Reading and Advanced Topics 8
2 Linear Learning Machines 9
2.1 Linear Classification 9
2.1.1 Rosenblatt's Perceptron 11
2.1.2 Other Linear Classifiers 19
2.1.3 Multi-class Discrimination 20
2.2 Linear Regression 20
2.2.1 Least Squares 21
2.2.2 Ridge Regression 22
2.3 Dual Representation of Linear Machines 24
2.4 Exercises 25
2.5 Further Reading and Advanced Topics 25
3 Kernel-Induced Feature Spaces 26
3.1 Learning in Feature Space 27
3.2 The Implicit Mapping into Feature Space 30
3.3 Making Kernels 32
3.3.1 Characterisation of Kernels 33
3.3.2 Making Kernels from Kernels 42
3.3.3 Making Kernels from Features 44
3.4 Working in Feature Space 46
vi Contents
3.5 Kernels and Gaussian Processes 48
3.6 Exercises 49
3.7 Further Reading and Advanced Topics 50
4 Generalisation Theory 52
4.1 Probably Approximately Correct Learning 52
4.2 Vapnik Chervonenkis (VC) Theory 54
4.3 Margin-Based Bounds on Generalisation 59
4.3.1 Maximal Margin Bounds 59
4.3.2 Margin Percentile Bounds 64
4.3.3 Soft Margin Bounds 65
4.4 Other Bounds on Generalisation and Luckiness 69
4.5 Generalisation for Regression 70
4.6 Bayesian Analysis of Learning 74
4.7 Exercises 76
4.8 Further Reading and Advanced Topics 76
5 Optimisation Theory 79
5.1 Problem Formulation 79
5.2 Lagrangian Theory 81
5.3 Duality 87
5.4 Exercises 89
5.5 Further Reading and Advanced Topics 90
6 Support Vector Machines 93
6.1 Support Vector Classification 93
6.1.1 The Maximal Margin Classifier 94
6.1.2 Soft Margin Optimisation 103
6.1.3 Linear Programming Support Vector Machines 112
6.2 Support Vector Regression 112
6.2.1 e-Insensitive Loss Regression 114
6.2.2 Kernel Ridge Regression 118
6.2.3 Gaussian Processes 120
6.3 Discussion 121
6.4 Exercises 121
6.5 Further Reading and Advanced Topics 122
7 Implementation Techniques 125
7.1 General Issues 125
7.2 The Naive Solution: Gradient Ascent 129
7.3 General Techniques and Packages 135
7.4 Chunking and Decomposition 136
7.5 Sequential Minimal Optimisation (SMO) 137
7.5.1 Analytical Solution for Two Points 138
7.5.2 Selection Heuristics 140
7.6 Techniques for Gaussian Processes 144
Contents vii
7.7 Exercises 145
7.8 Further Reading and Advanced Topics 146
8 Applications of Support Vector Machines 149
8.1 Text Categorisation 150
8.1.1 A Kernel from IR Applied to Information Filtering . . . . 150
8.2 Image Recognition 152
8.2.1 Aspect Independent Classification 153
8.2.2 Colour-Based Classification 154
8.3 Hand-written Digit Recognition 156
8.4 Bioinformatics 157
8.4.1 Protein Homology Detection 157
8.4.2 Gene Expression 159
8.5 Further Reading and Advanced Topics 160
A Pseudocode for the SMO Algorithm 162
B Background Mathematics 165
B.I Vector Spaces 165
B.2 Inner Product Spaces 167
B.3 Hilbert Spaces 169
B.4 Operators, Eigenvalues and Eigenvectors 171
References 173
Index 187 |
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| dewey-full | 006.31 |
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| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| edition | Repr. |
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| spelling | Cristianini, Nello 1968- Verfasser (DE-588)133181499 aut An introduction to support vector machines and other kernel-based learning methods Nello Cristianini and John Shawe-Taylor Repr. Cambridge [u.a.] Cambridge Univ. Press 2003 XIII, 189 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Lernendes System (DE-588)4120666-6 gnd rswk-swf Support-Vektor-Maschine (DE-588)4505517-8 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Lernendes System (DE-588)4120666-6 s Maschinelles Lernen (DE-588)4193754-5 s Support-Vektor-Maschine (DE-588)4505517-8 s 1\p DE-604 Shawe-Taylor, John 1953- Verfasser (DE-588)1151162264 aut HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=019935426&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Cristianini, Nello 1968- Shawe-Taylor, John 1953- An introduction to support vector machines and other kernel-based learning methods Lernendes System (DE-588)4120666-6 gnd Support-Vektor-Maschine (DE-588)4505517-8 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4120666-6 (DE-588)4505517-8 (DE-588)4193754-5 |
| title | An introduction to support vector machines and other kernel-based learning methods |
| title_auth | An introduction to support vector machines and other kernel-based learning methods |
| title_exact_search | An introduction to support vector machines and other kernel-based learning methods |
| title_full | An introduction to support vector machines and other kernel-based learning methods Nello Cristianini and John Shawe-Taylor |
| title_fullStr | An introduction to support vector machines and other kernel-based learning methods Nello Cristianini and John Shawe-Taylor |
| title_full_unstemmed | An introduction to support vector machines and other kernel-based learning methods Nello Cristianini and John Shawe-Taylor |
| title_short | An introduction to support vector machines |
| title_sort | an introduction to support vector machines and other kernel based learning methods |
| title_sub | and other kernel-based learning methods |
| topic | Lernendes System (DE-588)4120666-6 gnd Support-Vektor-Maschine (DE-588)4505517-8 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Lernendes System Support-Vektor-Maschine Maschinelles Lernen |
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