Kernels for structured data:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New Jersey [u.a.]
World Scientific
2008
|
| Schriftenreihe: | Series in machine perception and artificial intelligence
72 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Includes bibliographical references (p. 179-190) and index |
| Beschreibung: | XVII, 197 S. graph. Darst. |
| ISBN: | 9789812814555 9812814558 |
Internformat
MARC
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| 100 | 1 | |a Gärtner, Thomas |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Kernels for structured data |c Thomas Gärtner |
| 264 | 1 | |a New Jersey [u.a.] |b World Scientific |c 2008 | |
| 300 | |a XVII, 197 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 1 | |a Series in machine perception and artificial intelligence |v 72 | |
| 500 | |a Includes bibliographical references (p. 179-190) and index | ||
| 650 | 0 | 7 | |a Klassifikator |g Informatik |0 (DE-588)4288547-4 |2 gnd |9 rswk-swf |
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Datensatz im Suchindex
| _version_ | 1804139215685943296 |
|---|---|
| adam_text | Contents
Preface vii
Notational
Conventions xv
1.
Why Kernels for Structured Data?
1
1.1
Supervised Machine Learning
................ 2
1.2
Kernel Methods
........................ 4
1.3
Representing Structured Data
................ 6
1.4
Goals and Contributions
................... 7
1.5
Outline
............................ 8
1.6
Bibliographical Notes
.................... 9
2.
Kernel Methods in a Nutshell
13
2.1
Mathematical Foundations
.................. 14
2.1.1
From Sets to Functions
............... 14
2.1.2
Measures and Integrals
............... 15
2.1.3
Metric Spaces
.................... 17
2.1.4
Linear Spaces and Banach Spaces
......... 18
2.1.5
Inner Product Spaces and Hilbert Spaces
..... 20
2.1.6
Reproducing Kernels and Positive-Definite Functions
22
2.1.7
Matrix Computations
................ 24
2.1.8
Partitioned Inverse Equations
........... 26
2.2
Recognising Patterns with Kernels
............. 27
2.2.1
Supervised Learning
................. 27
2.2.2
Empirical Risk Minimisation
............ 29
2.2.3
Assessing Predictive Performance
......... 30
2.3
Foundations of Kernel Methods
............... 33
xii Kernels
for Structured Data
2.3.1
Model Fitting and Linear Inverse Equations
... 33
2.3.2
Common Grounds of Kernel Methods
....... 34
2.3.3
Représenter
Theorem
................ 36
2.4
Kernel Machines
....................... 38
2.4.1
Regularised Least Squares
............. 38
2.4.2
Support Vector Machines
.............. 41
2.4.3
Gaussian Processes
................. 45
2.4.4
Kernel Perceptron
.................. 47
2.4.5
Kernel Principal Component Analysis
....... 48
2.4.6
Distance-Based Algorithms
............. 51
2.5
Summary
........................... 52
3.
Kernel Design
55
3.1
General Remarks on Kernels and Examples
........ 56
3.1.1
Classes of Kernels
.................. 56
3.1.2
Good Kernels
.................... 57
3.1.3
Kernels on Inner Product Spaces
.......... 58
3.1.4
Some Illustrations
.................. 60
3.2
Kernel Functions
....................... 61
3.2.1
Closure Properties
.................. 62
3.2.2
Kernel Modifiers
................... 65
3.2.3
Minimal and Maximal Functions
.......... 66
3.2.4
Soft-Maximal Kernels
................ 67
3.3
Introduction to Kernels for Structured Data
........ 68
3.3.1
Intersection and
Crossproduct
Kernels on Sets
. . 68
3.3.2
Minimal and Maximal Functions on Sets
..... 71
3.3.3
Kernels on Multisets
................ 72
3.3.4
Convolution Kernels
................. 73
3.4
Prior Work
.......................... 73
3.4.1
Kernels from Generative Models
.......... 74
3.4.2
Kernels from Instance Space Graphs
........ 76
3.4.3
String Kernels
.................... 79
3.4.4
Tree Kernels
..................... 82
3.5
Summary
........................... 83
4.
Basic Term Kernels
85
4.1
Logics for Learning
...................... 86
4.1.1
Propositional Logic for Learning
.......... 86
Contents xiii
4.1.2 First-Order Logic
for Learning
........... 87
4.1.3
Lambda Calculus
.................. 88
4.1.4
Lambda Calculus with Polymorphic Types
.... 90
4.1.5
Basic Terms for Learning
.............. 91
4.2
Kernels for Basic Terms
................... 94
4.2.1
Default Kernels for Basic Terms
.......... 95
4.2.2
Positive Definiteness of the Default Kernel
.... 98
4.2.3
Specifying Kernels
.................. 101
4.3
Multi-Instance Learning
................... 103
4.3.1
The Multi-Instance Setting
............. 104
4.3.2
Separating MI Problems
.............. 105
4.3.3
Convergence of the MI Kernel Perceptron
..... 108
4.3.4
Alternative MI Kernels
...............
Ill
4.3.5
Learning MI Ray Concepts
............. 113
4.4
Related Work
......................... 114
4.4.1
Kernels for General Data Structures
........ 114
4.4.2
Multi-Instance Learning
.............. 115
4.5
Applications and Experiments
................ 116
4.5.1
East/West Challenge
................ 116
4.5.2
Drug Activity Prediction
.............. 117
4.5.3
Structure Elucidation from
Spectroscopie
Analyses
120
4.5.4
Spatial Clustering
.................. 123
4.6
Summary
........................... 125
Graph Kernels
127
5.1
Motivation and Approach
.................. 128
5.2
Labelled Directed Graphs
.................. 130
5.2.1
Basic Terminology and Notation
.......... 131
5.2.2
Matrix Notation and some Functions
....... 132
5.2.3
Product Graphs
................... 133
5.2.4
Limits of Matrix Power Series
........... 133
5.3
Complete Graph Kernels
................... 135
5.4
Walk Kernels
......................... 139
5.4.1
Kernels Based on Label Pairs
........... 140
5.4.2
Kernels Based on Contiguous Label Sequences
. . 142
5.4.3
Transition Graphs
.................. 143
5.4.4
Non-
Contiguous Label Sequences
......... 144
5.5
Cyclic Pattern Kernels
.................... 146
5.5.1
Undirected Graphs
................. 146
xiv Kernels
for Structured Data
5.5.2
Kernel Definition
.................. 147
5.5.3
Kernel Computation
................. 148
5.6
Related Work
......................... 151
5.7
Relational Reinforcement Learning
............. 154
5.7.1
Relational Reinforcement Learning
........ 155
5.7.2
Kernels for Graphs with Parallel Edges
...... 157
5.7.3
Kernel Based RRL in the Blocks World
...... 158
5.7.4
Experiments
..................... 161
5.7.5
Future Work
..................... 165
5.8
Molecule Classification
.................... 166
5.8.1
Mutagenicity
..................... 166
5.8.2 HIV
Data
...................... 168
5.9
Summary
........................... 172
6.
Conclusions
175
Bibliography
179
Index
191
|
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| id | DE-604.BV035564944 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T21:40:33Z |
| institution | BVB |
| isbn | 9789812814555 9812814558 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-017620611 |
| oclc_num | 465175453 |
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| owner | DE-473 DE-BY-UBG DE-703 |
| owner_facet | DE-473 DE-BY-UBG DE-703 |
| physical | XVII, 197 S. graph. Darst. |
| publishDate | 2008 |
| publishDateSearch | 2008 |
| publishDateSort | 2008 |
| publisher | World Scientific |
| record_format | marc |
| series | Series in machine perception and artificial intelligence |
| series2 | Series in machine perception and artificial intelligence |
| spelling | Gärtner, Thomas Verfasser aut Kernels for structured data Thomas Gärtner New Jersey [u.a.] World Scientific 2008 XVII, 197 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Series in machine perception and artificial intelligence 72 Includes bibliographical references (p. 179-190) and index Klassifikator Informatik (DE-588)4288547-4 gnd rswk-swf Strukturierte Daten (DE-588)4620514-7 gnd rswk-swf Strukturierte Daten (DE-588)4620514-7 s Klassifikator Informatik (DE-588)4288547-4 s DE-604 Series in machine perception and artificial intelligence 72 (DE-604)BV006668231 72 Digitalisierung UB Bamberg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017620611&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Gärtner, Thomas Kernels for structured data Series in machine perception and artificial intelligence Klassifikator Informatik (DE-588)4288547-4 gnd Strukturierte Daten (DE-588)4620514-7 gnd |
| subject_GND | (DE-588)4288547-4 (DE-588)4620514-7 |
| title | Kernels for structured data |
| title_auth | Kernels for structured data |
| title_exact_search | Kernels for structured data |
| title_full | Kernels for structured data Thomas Gärtner |
| title_fullStr | Kernels for structured data Thomas Gärtner |
| title_full_unstemmed | Kernels for structured data Thomas Gärtner |
| title_short | Kernels for structured data |
| title_sort | kernels for structured data |
| topic | Klassifikator Informatik (DE-588)4288547-4 gnd Strukturierte Daten (DE-588)4620514-7 gnd |
| topic_facet | Klassifikator Informatik Strukturierte Daten |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017620611&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV006668231 |
| work_keys_str_mv | AT gartnerthomas kernelsforstructureddata |