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
2010
|
| Ausgabe: | 1. publ., 11. printing |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Inhaltsverzeichnis |
| Beschreibung: | Literaturverz. S. 173 - 186. - Hier auch später erschienene, unveränderte Nachdrucke |
| Beschreibung: | XIII, 189 S. Ill., graph. Darst. 26 cm |
| ISBN: | 0521780195 9780521780193 |
Internformat
MARC
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|---|---|---|---|
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| 003 | DE-604 | ||
| 005 | 20120320 | ||
| 007 | t | ||
| 008 | 100211s2010 ad|| |||| 00||| eng d | ||
| 020 | |a 0521780195 |9 0-521-78019-5 | ||
| 020 | |a 9780521780193 |9 978-0-521-78019-3 | ||
| 035 | |a (OCoLC)549306473 | ||
| 035 | |a (DE-599)GBV615302572 | ||
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| 100 | 1 | |a Cristianini, Nello |d 1968- |e Verfasser |0 (DE-588)133181499 |4 aut | |
| 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., 11. printing | ||
| 264 | 1 | |a Cambridge [u.a.] |b Cambridge Univ. Press |c 2010 | |
| 300 | |a XIII, 189 S. |b Ill., graph. Darst. |c 26 cm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Literaturverz. S. 173 - 186. - Hier auch später erschienene, unveränderte Nachdrucke | ||
| 650 | 4 | |a Lernendes System - Maschinelles Lernen - Support-Vektor-Maschine | |
| 650 | 0 | 7 | |a Support-Vektor-Maschine |0 (DE-588)4505517-8 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Lernendes System |0 (DE-588)4120666-6 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |2 gnd |9 rswk-swf |
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| 689 | 0 | 1 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |D s |
| 689 | 0 | 2 | |a Support-Vektor-Maschine |0 (DE-588)4505517-8 |D s |
| 689 | 0 | |5 DE-604 | |
| 700 | 1 | |a Shawe-Taylor, John |e Verfasser |4 aut | |
| 856 | 4 | |u http://www.gbv.de/dms/goettingen/512565724.pdf |z lizenzfrei |3 Inhaltsverzeichnis | |
| 856 | 4 | 2 | |m Digitalisierung UB Bayreuth |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018922583&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-018922583 | ||
Datensatz im Suchindex
| _version_ | 1804141052701966336 |
|---|---|
| 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
24
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
Vápnik
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
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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| author | Cristianini, Nello 1968- Shawe-Taylor, John |
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| dewey-full | 006.3121 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.3121 |
| dewey-search | 006.3121 |
| dewey-sort | 16.3121 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| edition | 1. publ., 11. printing |
| format | Book |
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| id | DE-604.BV036030504 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T22:09:45Z |
| institution | BVB |
| isbn | 0521780195 9780521780193 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-018922583 |
| oclc_num | 549306473 |
| open_access_boolean | |
| owner | DE-703 DE-526 DE-355 DE-BY-UBR |
| owner_facet | DE-703 DE-526 DE-355 DE-BY-UBR |
| physical | XIII, 189 S. Ill., graph. Darst. 26 cm |
| publishDate | 2010 |
| publishDateSearch | 2010 |
| publishDateSort | 2010 |
| publisher | Cambridge Univ. Press |
| record_format | marc |
| 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., 11. printing Cambridge [u.a.] Cambridge Univ. Press 2010 XIII, 189 S. Ill., graph. Darst. 26 cm txt rdacontent n rdamedia nc rdacarrier Literaturverz. S. 173 - 186. - Hier auch später erschienene, unveränderte Nachdrucke Lernendes System - Maschinelles Lernen - Support-Vektor-Maschine Support-Vektor-Maschine (DE-588)4505517-8 gnd rswk-swf Lernendes System (DE-588)4120666-6 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 DE-604 Shawe-Taylor, John Verfasser aut http://www.gbv.de/dms/goettingen/512565724.pdf lizenzfrei Inhaltsverzeichnis Digitalisierung UB Bayreuth application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018922583&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Cristianini, Nello 1968- Shawe-Taylor, John An introduction to support vector machines and other kernel-based learning methods Lernendes System - Maschinelles Lernen - Support-Vektor-Maschine Support-Vektor-Maschine (DE-588)4505517-8 gnd Lernendes System (DE-588)4120666-6 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4505517-8 (DE-588)4120666-6 (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 and other kernel-based learning methods |
| title_sort | an introduction to support vector machines and other kernel based learning methods |
| topic | Lernendes System - Maschinelles Lernen - Support-Vektor-Maschine Support-Vektor-Maschine (DE-588)4505517-8 gnd Lernendes System (DE-588)4120666-6 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Lernendes System - Maschinelles Lernen - Support-Vektor-Maschine Support-Vektor-Maschine Lernendes System Maschinelles Lernen |
| url | http://www.gbv.de/dms/goettingen/512565724.pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018922583&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT cristianininello anintroductiontosupportvectormachinesandotherkernelbasedlearningmethods AT shawetaylorjohn anintroductiontosupportvectormachinesandotherkernelbasedlearningmethods |
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