An introduction to support vector machines and other kernel-based learning methods:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge [u.a.]
Cambridge Univ. Press
2006
|
| Ausgabe: | 1. publ., 10. printing |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Literaturverzeichnis Seite 173 - 186 Hier auch später erschienene, unveränderte Nachdrucke |
| Beschreibung: | XIII, 189 S. Illustrationen, Diagramme |
| ISBN: | 0521780195 |
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| 245 | 1 | 0 | |a An introduction to support vector machines and other kernel-based learning methods |c Nello Cristianini and John Shawe-Taylor |
| 250 | |a 1. publ., 10. printing | ||
| 264 | 1 | |a Cambridge [u.a.] |b Cambridge Univ. Press |c 2006 | |
| 300 | |a XIII, 189 S. |b Illustrationen, Diagramme | ||
| 336 | |b txt |2 rdacontent | ||
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| 500 | |a Literaturverzeichnis Seite 173 - 186 | ||
| 500 | |a Hier auch später erschienene, unveränderte Nachdrucke | ||
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Datensatz im Suchindex
| _version_ | 1804135399479574528 |
|---|---|
| adam_text | 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
3.5 Kernels
and Gaussian Processes
.................... 48
3.6
Exercises
.................................. 49
3.7
Further Reading and Advanced Topics
................ 50
Generalisation Theory
52
4.1
Probably Approximately Correct Learning
.............. 52
4.2
Vápnik
Chervonenkis (VC) Theory
.................. 54
4.3
Margin-Based Bounds on Generalisation
............... 59
4.3.1
Maximal Margin Bounds
.................... 59
4.3.2
Margin
Percentne
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
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
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
ε
-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
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
В
Background Mathematics
165
B.I Vector Spaces
............................... 165
B.2 Inner Product Spaces
.......................... 167
B.3 Hubert Spaces
.............................. 169
B.4 Operators, Eigenvalues and Eigenvectors
............... 171
References
173
Index
187
|
| adam_txt |
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
3.5 Kernels
and Gaussian Processes
. 48
3.6
Exercises
. 49
3.7
Further Reading and Advanced Topics
. 50
Generalisation Theory
52
4.1
Probably Approximately Correct Learning
. 52
4.2
Vápnik
Chervonenkis (VC) Theory
. 54
4.3
Margin-Based Bounds on Generalisation
. 59
4.3.1
Maximal Margin Bounds
. 59
4.3.2
Margin
Percentne
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
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
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
ε
-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
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
В
Background Mathematics
165
B.I Vector Spaces
. 165
B.2 Inner Product Spaces
. 167
B.3 Hubert Spaces
. 169
B.4 Operators, Eigenvalues and Eigenvectors
. 171
References
173
Index
187 |
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| discipline | Informatik |
| discipline_str_mv | Informatik |
| edition | 1. publ., 10. printing |
| format | Book |
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| index_date | 2024-07-02T14:51:09Z |
| indexdate | 2024-07-09T20:39:53Z |
| institution | BVB |
| isbn | 0521780195 |
| language | English |
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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 1. publ., 10. printing Cambridge [u.a.] Cambridge Univ. Press 2006 XIII, 189 S. Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Literaturverzeichnis Seite 173 - 186 Hier auch später erschienene, unveränderte Nachdrucke Algorytmy jhpk Jądra (analiza funkcjonalna) jhpk Uczenie się automatyczne jhpk Lernendes System (DE-588)4120666-6 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Support-Vektor-Maschine (DE-588)4505517-8 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 Digitalisierung UB Passau application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014827826&sequence=000002&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 Algorytmy jhpk Jądra (analiza funkcjonalna) jhpk Uczenie się automatyczne jhpk Lernendes System (DE-588)4120666-6 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Support-Vektor-Maschine (DE-588)4505517-8 gnd |
| subject_GND | (DE-588)4120666-6 (DE-588)4193754-5 (DE-588)4505517-8 |
| 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_exact_search_txtP | 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 and other kernel-based learning methods |
| title_sort | an introduction to support vector machines and other kernel based learning methods |
| topic | Algorytmy jhpk Jądra (analiza funkcjonalna) jhpk Uczenie się automatyczne jhpk Lernendes System (DE-588)4120666-6 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Support-Vektor-Maschine (DE-588)4505517-8 gnd |
| topic_facet | Algorytmy Jądra (analiza funkcjonalna) Uczenie się automatyczne Lernendes System Maschinelles Lernen Support-Vektor-Maschine |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014827826&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT cristianininello anintroductiontosupportvectormachinesandotherkernelbasedlearningmethods AT shawetaylorjohn anintroductiontosupportvectormachinesandotherkernelbasedlearningmethods |