Semi-supervised learning:
In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of a...
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| Weitere Verfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge, Massachusetts ; London, England
The MIT Press
2010
|
| Ausgabe: | First MIT Press paperback edition |
| Schriftenreihe: | Adaptive computation and machine learning
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Zusammenfassung: | In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research. Semi-Supervised Learning first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low-density separation assumption, graph-based methods, and algorithms that perform two-step learning. The book then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of extensive benchmark experiments. Finally, the book looks at interesting directions for SSL research. The book closes with a discussion of the relationship between semi-supervised learning and transduction. |
| Beschreibung: | Beziehunskennzeichnungen laut Einband: edited by Olivier Chapelle, Bernhard Schölkopf and Alexander Zien Includes bibliographical references and index |
| Beschreibung: | x, 508 Seiten Illustrationen, Diagramme |
| ISBN: | 9780262033589 9780262514125 |
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| 245 | 1 | 0 | |a Semi-supervised learning |c Olivier Chapelle, Bernhard Schölkopf, Alexander Zien |
| 250 | |a First MIT Press paperback edition | ||
| 264 | 1 | |a Cambridge, Massachusetts ; London, England |b The MIT Press |c 2010 | |
| 300 | |a x, 508 Seiten |b Illustrationen, Diagramme | ||
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| 490 | 0 | |a Adaptive computation and machine learning | |
| 500 | |a Beziehunskennzeichnungen laut Einband: edited by Olivier Chapelle, Bernhard Schölkopf and Alexander Zien | ||
| 500 | |a Includes bibliographical references and index | ||
| 520 | 3 | |a In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research. Semi-Supervised Learning first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low-density separation assumption, graph-based methods, and algorithms that perform two-step learning. The book then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of extensive benchmark experiments. Finally, the book looks at interesting directions for SSL research. The book closes with a discussion of the relationship between semi-supervised learning and transduction. | |
| 650 | 4 | |a Apprentissage supervisé (Intelligence artificielle) | |
| 650 | 4 | |a Supervised learning (Machine learning) | |
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| 999 | |a oai:aleph.bib-bvb.de:BVB01-019009040 | ||
Datensatz im Suchindex
| _version_ | 1804141224302477312 |
|---|---|
| adam_text | Contents
Series
Foreword
xi
Preface
xiii
Introduction to Semi-Supervised Learning
1
1.1
Supervised, Unsupervised, and Semi-Supervised Learning
...... 1
1.2
When Can Semi-Supervised Learning Work?
............. 4
1.3
Classes of Algorithms and Organization of This Book
........ 8
Generative Models
13
A Taxonomy for Semi-Supervised Learning Methods
15
Matthias Seeger
2.1
The Semi-Supervised Learning Problem
................ 15
2.2
Paradigms for Semi-Supervised Learning
................ 17
2.3
Examples
................................. 22
2.4
Conclusions
................................ 31
Semi-Supervised Text Classification Using EM
33
Kamal Nigam, Andrew McCallum, Tom Mitchell
3.1
Introduction
................................ 33
3.2
A Generative Model for Text
...................... 35
3.3
Experimental Results with Basic EM
.................. 41
3.4
Using a More Expressive Generative Model
.............. 43
3.5
Overcoming the Challenges of Local Maxima
............. 49
3.6
Conclusions and Summary
........................ 54
Risks of Semi-Supervised Learning
57
Fabio
Cozman, Ira Cohen
4.1
Do Unlabeled Data Improve or Degrade Classification Performance?
57
4.2
Understanding Unlabeled Data: Asymptotic Bias
........... 59
4.3
The Asymptotic Analysis of Generative Semi-Supervised Learning
. 63
4.4
The Value of Labeled and Unlabeled Data
............... 67
4.5
Finite Sample Effects
........................... 69
Contents
4.6 Model
Search and Robustness
...................... 70
4.7
Conclusion
................................ 71
Probabilistic Semi-Supervised Clustering with Constraints
73
Sugato
Basu,
Mikhail Bilenko, Arindam Banerjee, Raymond Mooney
5.1
Introduction
................................ 74
5.2
HMRF Model for Semi-Supervised Clustering
............. 75
5.3
HMRF-KMeans Algorithm
...................... 81
5.4
Active Learning for Constraint Acquisition
.............. 93
5.5
Experimental Results
........................... 96
5.6
Related Work
............................... 100
5.7
Conclusions
................................ 101
II Low-Density Separation
103
6
Transductive Support Vector Machines
105
Thorsten
Joachims
6.1
Introduction
................................105
6.2
Transductive Support Vector Machines
.................108
6.3
Why Use Margin on the Test Set?
...................
Ill
6.4
Experiments and Applications of TSVMs
...............112
6.5
Solving the TSVM Optimization Problem
...............114
6.6
Connection to Related Approaches
...................116
6.7
Summary and Conclusions
........................116
7
Semi-Supervised Learning Using Semi-Definite Programming
119
Tijl
De Bie, Nello Cristianini
7.1
Relaxing SVM Transduction
....................... 119
7.2
An Approximation for Speedup
..................... 126
7.3
General Semi-Supervised Learning Settings
.............. 128
7.4
Empirical Results
............................. 129
7.5
Summary and Outlook
.......................... 133
Appendix: The Extended
Schur
Complement Lemma
......... 134
8
Gaussian Processes and the Null-Category Noise Model
137
Neil D. Lawrence, Michael I. Jordan
8.1
Introduction
................................ 137
8.2
The Noise Model
............................. 141
8.3
Process Model and Effect of the Null-Category
............ 143
8.4
Posterior Inference and Prediction
................... 145
8.5
Results
................................... 147
8.6
Discussion
................................. 149
9
Entropy Regularization
151
Contenta
vii
Yves
Grandvalet,
Yoshua
Вепдго
9.1
Introduction
................................ 151
9.2
Derivation of the Criterion
........................ 152
9.3
Optimization Algorithms
........................ 155
9.4
Related Methods
............................. 158
9.5
Experiments
................................ 160
9.6
Conclusion
................................ 166
Appendix: Proof of Theorem
9.1 .................... 166
10
Data-Dependent Regularization
169
Adrian Corduneanu, Tommi Jaakkola
10.1
Introduction
................................ 169
10.2
Information Regularization on Metric Spaces
............. 174
10.3
Information Regularization and Relational Data
............ 182
10.4
Discussion
................................. 189
III Graph-Based Methods
191
11
Label Propagation and Quadratic Criterion
193
Yoshua
Bangio,
Olivier Delalleau, Nicolas
Le, Roux
11.1
Introduction
................................ 193
11.2
Label Propagation on a Similarity Graph
............... 194
11.3
Quadratic Cost Criterion
........................ 198
11.4
From Transduction to Induction
.................... 205
11.5
Incorporating Class Prior Knowledge
.................. 205
11.6
Curse of Dimensionality for Semi-Supervised Learning
........ 206
11.7
Discussion
................................. 215
12
The Geometric Basis of Semi-Supervised Learning
217
Vikas Sindhwani,
Misha Belkin, Partha Niyogi
12.1
Introduction
................................ 217
12.2
Incorporating Geometry in Regularization
............... 220
12.3
Algorithms
................................ 224
12.4
Data-Dependent Kernels for Semi-Supervised Learning
........ 229
12.5
Linear Methods for Large-Scale Semi-Supervised Learning
...... 231
12.6
Connections to Other Algorithms and Related Work
......... 232
12.7
Future Directions
............................. 234
13
Discrete Regularization
237
Dengyong Zhou.
Bernhard
Schölkopf
13.1
Introduction
................................237
13.2
Discrete Analysis
.............................239
13.3
Discrete Regularization
.........................245
13.4
Conclusion
................................249
viii Contents
14
Semi-Supervised Learning with Conditional Harmonic Mixing
251
Christopher J.
C. Burges,
John
C. Platt
14.1
Introduction
................................ 251
14.2
Conditional Harmonic Mixing
...................... 255
14.3
Learning in CHM Models
........................ 256
14.4
Incorporating Prior Knowledge
..................... 261
14.5
Learning the Conditionals
........................ 261
14.6
Model Averaging
............................. 262
14.7
Experiments
................................ 263
14.8
Conclusions
................................ 273
IV Change of Representation
275
15
Graph Kernels by Spectral Transforms
277
Xiaojin Zhu,
Jaz
Kandola, John Lafferty, Zoubin Ghahramani
15.1
The Graph Laplacian
........................... 278
15.2
Kernels by Spectral Transforms
..................... 280
15.3
Kernel Alignment
............................. 281
15.4
Optimizing Alignment Using QCQP for Semi-Supervised Learning
. 282
15.5
Semi-Supervised Kernels with Order Constraints
........... 283
15.6
Experimental Results
........................... 285
15.7
Conclusion
................................ 289
16
Spectral Methods for Dimensionality
Reduction
293
Lawrence
К.
Saul,
Kilián
Q.
Weinberger,
Fei
Sha,
Jihun Ham, Daniel
D.
Lee
16.1
Introduction
................................293
16.2
Linear Methods
..............................295
16.3
Graph-Based Methods
..........................297
16.4
Kernel Methods
..............................303
16.5
Discussion
.................................306
17
Modifying Distances
309
Sajama, Alon Orlitsky
17.1
Introduction
................................309
17.2
Estimating DBD Metrics
.........................312
17.3
Computing DBD Metrics
........................321
17.4
Semi-Supervised Learning Using Density-Based Metrics
.......327
17.5
Conclusions and Future Work
......................329
V Semi-Supervised Learning in Practice
331
18
Large-Scale Algorithms
333
Contents
Olivier Delalleau, Yoshua Bengio, Nicolas Le Roux
18.1
Introduction
................................ 333
18.2
Cost Approximations
........................... 334
18.3
Subset Selection .............................
337
18.4
Discussion
................................. 340
19
Semi-Supervised Protein Classification
Using Cluster Kernels
, 343
Jason Weston, Christina Leslie, Eugene
le,
William Stafford Noble
19.1
Introduction
................................ 343
19.2
Representations and Kernels for Protein Sequences
.......... 345
19.3
Semi-Supervised Kernels for Protein Sequences
............ 348
19.4
Experiments
................................ 352
19.5
Discussion
................................. 358
20
Prediction of Protein Function from
Networks
361
Hyunjxing Shin,
Koji
Tsuda
20.1
Introduction
................................ 361
20.2
Graph-Based Semi-Supervised Learning
................ 304
20.3
Combining Multiple Graphs
....................... 366
20.4
Experiments on Function Prediction of Proteins
............ 309
20.5
Conclusion and Outlook
......................... 374
21
Analysis of Benchmarks
377
21.1
The Benchmark
.............................. 377
21.2
Application of SSL Methods
....................... 383
21.3
Results and Discussion
.......................... 390
VI Perspectives
395
22
An Augmented
PAC
Model for Semi-Supervised Learning
397
Maria-Fiorina
Balean,
Avrim Blum
22.1
Introduction
................................398
22.2
A Formal Framework
...........................400
22.3
Sample Complexity Results
.......................403
22.4
Algorithmic Results
...........................412
22.5
Related Models and Discussion
.....................416
23
Metric-Based Approaches for Semi-
Supervised Regression and Classification
421
Dale Schuurmans.
Finnegan
Southey. Dana Wilkinson. Yuhong Guo
23.1
Introduction
................................421
23.2
Metric Structure of Supervised Learning
................423
Contents
23.3 Model
Selection..............................
426
23.4 Regularization .............................. 436
23.5
Classification...............................
445
23.6
Conclusion
................................ 449
24 Transductive
Inference and
Semi-Supervised Learning
453
Vladimir
Vápnik
24.1
Problem Settings
.............................453
24.2
Problem of Generalization in Inductive and Transductive Inference
. 455
24.3
Structure of the VC Bounds and Transductive Inference
.......457
24.4
The Symmetrization Lemma and Transductive Inference
.......458
24.5
Bounds for Transductive Inference
...................459
24.6
The Structural Risk Minimization Principle for Induction and Trans-
duction
..................................460
24.7
Combinatorics in Transductive Inference
................462
24.8
Measures of the Size of Equivalence Classes
..............463
24.9
Algorithms for Inductive and Transductive SVMs
...........465
24.10
Semi-Supervised Learning
.......................470
24.11
Conclusion: Transductive Inference and the New Problems of Infer¬
ence
....................................470
24.12
Beyond Transduction: Selective Inference
...............471
25
A Discussion of Semi-Supervised Learning and Transduction
473
References
479
Notation and Symbols
499
Contributors
503
Index
509
|
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| discipline | Informatik |
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| id | DE-604.BV036119012 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T22:12:28Z |
| institution | BVB |
| isbn | 9780262033589 9780262514125 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-019009040 |
| oclc_num | 457159745 |
| open_access_boolean | |
| owner | DE-19 DE-BY-UBM DE-355 DE-BY-UBR DE-83 DE-11 |
| owner_facet | DE-19 DE-BY-UBM DE-355 DE-BY-UBR DE-83 DE-11 |
| physical | x, 508 Seiten Illustrationen, Diagramme |
| publishDate | 2010 |
| publishDateSearch | 2010 |
| publishDateSort | 2010 |
| publisher | The MIT Press |
| record_format | marc |
| series2 | Adaptive computation and machine learning |
| spelling | Semi-supervised learning Olivier Chapelle, Bernhard Schölkopf, Alexander Zien First MIT Press paperback edition Cambridge, Massachusetts ; London, England The MIT Press 2010 x, 508 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Adaptive computation and machine learning Beziehunskennzeichnungen laut Einband: edited by Olivier Chapelle, Bernhard Schölkopf and Alexander Zien Includes bibliographical references and index In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research. Semi-Supervised Learning first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low-density separation assumption, graph-based methods, and algorithms that perform two-step learning. The book then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of extensive benchmark experiments. Finally, the book looks at interesting directions for SSL research. The book closes with a discussion of the relationship between semi-supervised learning and transduction. Apprentissage supervisé (Intelligence artificielle) Supervised learning (Machine learning) Überwachtes Lernen (DE-588)4580264-6 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Überwachtes Lernen (DE-588)4580264-6 s DE-604 Maschinelles Lernen (DE-588)4193754-5 s Chapelle, Olivier edt Schölkopf, Bernhard 1968- (DE-588)14184633X edt Zien, Alexander 1971- (DE-588)128914521 edt Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=019009040&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Semi-supervised learning Apprentissage supervisé (Intelligence artificielle) Supervised learning (Machine learning) Überwachtes Lernen (DE-588)4580264-6 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4580264-6 (DE-588)4193754-5 |
| title | Semi-supervised learning |
| title_auth | Semi-supervised learning |
| title_exact_search | Semi-supervised learning |
| title_full | Semi-supervised learning Olivier Chapelle, Bernhard Schölkopf, Alexander Zien |
| title_fullStr | Semi-supervised learning Olivier Chapelle, Bernhard Schölkopf, Alexander Zien |
| title_full_unstemmed | Semi-supervised learning Olivier Chapelle, Bernhard Schölkopf, Alexander Zien |
| title_short | Semi-supervised learning |
| title_sort | semi supervised learning |
| topic | Apprentissage supervisé (Intelligence artificielle) Supervised learning (Machine learning) Überwachtes Lernen (DE-588)4580264-6 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Apprentissage supervisé (Intelligence artificielle) Supervised learning (Machine learning) Überwachtes Lernen Maschinelles Lernen |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=019009040&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT chapelleolivier semisupervisedlearning AT scholkopfbernhard semisupervisedlearning AT zienalexander semisupervisedlearning |