Optimization for machine learning:
Gespeichert in:
| Weitere Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge, Mass. [u.a.]
MIT Press
2012
|
| Schriftenreihe: | Neural information processing series
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | IX, 494 S. Ill., graph. Darst. |
| ISBN: | 9780262016469 9780262537766 |
Internformat
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| 245 | 1 | 0 | |a Optimization for machine learning |c ed. by Suvrit Sra ... |
| 264 | 1 | |a Cambridge, Mass. [u.a.] |b MIT Press |c 2012 | |
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Datensatz im Suchindex
| _version_ | 1804149509796659200 |
|---|---|
| adam_text | Contents
Series
Foreword
xi
Preface
xiii
1
Introduction: Optimization and Machine Learning
S.
Sra,
S.
Nowozin, and S. J. Wright
1
1.1
Support Vector Machines
.................... 2
1.2
Regularized Optimization
.................... 7
1.3
Summary of the Chapters
.................... 11
1.4
References
............................. 15
2
Convex Optimization with Sparsity-Inducing Norms
F. Bach, R.
Jenatton,
J. Mairal, and
G. Obozinski
19
2.1
Introduction
............................ 19
2.2
Generic Methods
......................... 26
2.3
Proximal Methods
........................ 27
2.4
(Block) Coordinate Descent Algorithms
............ 32
2.5
Reweighted-^2 Algorithms
. . .................. 34
2.6
Working-Set Methods
...................... 36
2.7
Quantitative Evaluation
..................... 40
2.8
Extensions
............................. 47
2.9
Conclusion
............................ 48
2.10
References
............................. 49
3
Interior-Point Methods for Large-Scale Cone Programming
M. Andersen, J.
Dahl,
Z.
Liu, and L. Vandenberghe
55
3.1
Introduction
............................. 56
3.2
Primal-Dual Interior-Point Methods
.............. 60
3.3
Linear and Quadratic Programming
.............. 64
3.4
Second-Order Cone Programming
................ 71
3.5
Semidefinite
Programming
.................... 74
3.6
Conclusion
........................... 79
3.7
References
............................. 79
Incremental Gradient,
Subgradient,
and Proximal Methods
for Convex Optimization: A Survey
D. P.
Bertsekas
85
4.1
Introduction
............................ 86
4.2
Incremental Subgradient-Proximal Methods
.......... 98
4.3
Convergence for Methods with Cyclic Order
.......... 102
4.4
Convergence for Methods with Randomized Order
...... 108
4.5
Some Applications
........................
Ill
4.6
Conclusions
............................ 114
4.7
References
............................. 115
First-Order Methods for Nonsmooth Convex Large-Scale
Optimization, I: General Purpose Methods
A. Juditsky and A. Nemirovski
121
5.1
Introduction
............................ 121
5.2
Mirror Descent Algorithm: Minimizing over a Simple Set
. . . 126
5.3
Problems with Functional Constraints
............. 130
5.4
Minimizing Strongly Convex Functions
............. 131
5.5
Mirror Descent Stochastic Approximation
........... 134
5.6
Mirror Descent for Convex-Concave Saddle-Point Problems
. 135
5.7
Setting up a Mirror Descent Method
. ............. 139
5.8
Notes and Remarks
........................ 145
5.9
References
............................. 146
First-Order Methods for Nonsmooth Convex Large-Scale
Optimization, II: Utilizing Problem s Structure
A. Juditsky and A. Nemirovski
149
6.1
Introduction
............................ 149
6.2
Saddle-Point Reformulations of Convex Minimization Problemsl51
6.3
Mirror-Prox Algorithm
...................... 154
6.4
Accelerating the Mirror-Prox Algorithm
............ 160
6.5
Accelerating First-Order Methods by Randomization
..... 171
6.6
Notes and Remarks
........................ 179
6.7
References
............................. 181
Cutting-Plane Methods in Machine Learning
V. Franc, S.
Sonnenburg,
and T. Werner
185
7.1
Introduction to Cutting-plane Methods
............ 187
7.2
Regularized Risk Minimization
................. 191
7.3
Multiple Kernel Learning
.................... 197
7.4
MAP Inference in Graphical Models
..............203
7.5
References
.............................214
і
Introduction to Dual Decomposition for Inference
D.
Sontag,
A. Globerson, and T. Jaakkola
219
8.1
Introduction
............................ 220
8.2
Motivating Applications
..................... 222
8.3
Dual Decomposition and Lagrangian Relaxation
....... 224
8.4
Subgradient
Algorithms
..................... 229
8.5
Block Coordinate Descent Algorithms
............. 232
8.6
Relations to Linear Programming Relaxations
......... 240
8.7
Decoding: Finding the MAP Assignment
............ 242
8.8
Discussion
............................ 245
8.10
References
............................. 252
9
Augmented Lagrangian Methods for Learning, Selecting,
and Combining Features
R. Tomioka, T. Suzuki, and M. Sugiyama
255
9.1
Introduction
............................ 256
9.2
Background
............................ 258
9.3
Proximal Minimization Algorithm
............... 263
9.4
Dual Augmented Lagrangian (DAL) Algorithm
........ 265
9.5
Connections
............................ 272
9.6
Application
............................ 276
9.7
Summary
............................. 280
9.9
References
............................. 282
10
The Convex Optimization Approach to Regret
Minimization
E. Hazan
287
10.1
Introduction
............................ 287
10.2
The RFTL Algorithm and Its Analysis
............. 291
10.3
The Primal-Dual Approach
.................. 294
10.4
Convexity of Loss Functions
................... 298
10.5
Recent Applications
....................... 300
10.6
References
............................. 302
11
Projected Newton-type Methods in Machine Learning
M. Schmidt, D. Kim, and S.
Sra
305
11.1
Introduction
............................305
11.2
Projected Newton-type Methods
................306
11.3
Two-Metric Projection Methods
................312
11.4
Inexact
Projection
Methods
................... 316
11.5
Toward
Nonsmoot
h
Objectives
................. 320
11.6
Summary and Discussion
.................... 326
11.7
References
............................. 327
12
Interior-Point Methods in Machine Learning
,].
Gondzio
331
12.1
Introduction
............................ 331
12.2
Interior-Point Methods: Background
.............. 333
12.3
Polynomial Complexity Result
................. 337
12.4
Interior-Point Methods for Machine Learning
......... 338
12.5
Accelerating Interior-Point Methods
.............. 344
12.6
Conclusions
............................ 347
12.7
References
............................. 347
13
The Tradeoffs of Large-Scale Learning
L. Bottou and O.
Bousquet
351
13.1
Introduction
............................ 351
13.2
Approximate Optimization
................... 352
13.3
Asymptotic Analysis
....................... 355
13.4
Experiments
............................ 363
13.5
Conclusion
............................ 366
13.6
References
............................. 367
14
Robust Optimization in Machine Learning
C. Caramanis, S. Manner, and H. Xu
369
14.1
Introduction
............................ 370
14.2
Background on Robust Optimization
.............. 371
14.3
Robust Optimization and Adversary Resistant Learning
. . . 373
14.4
Robust Optimization and Regularization
............ 377
14.5
Robustness and Consistency
................... 390
14.6
Robustness and Generalization
................. 394
14.7
Conclusion
............................ 399
14.8
References
............................. 399
15
Improving First and Second-Order Methods by Modeling
Uncertainty
N.
Le Roux. Y.
Bengio, and A. Fitzgibbon
403
15.1
Introduction
............................ 403
15.2
Optimization Versus Learning
.................. 404
15.3
Building a Model of the Gradients
............... 406
15.4
The Relative Roles of the Covariance and the Hessian
.... 409
15.5
A Second-Order Model of the Gradients
............ 412
15.6
An Efficient Implementation of Online Consensus Gradient:
TONGA
.............................. 414
15.7
Experiments
............................ 419
15.8
Conclusion
............................ 427
15.9
References
............................. 429
16
Bandit View on Noisy Optimization
J.-Y.
Audibert.
S.
Bubeck,
and
R. Munos
431
16.1
Introduction
............................ 431
16.2
Concentration Inequalities
.................... 433
16.3
Discrete Optimization
...................... 434
16.4
Online Optimization
....................... 443
16.5
References
............................. 452
17
Optimization Methods for Sparse Inverse Covariance
Selection
K.
Scheinberg
and
S.
Ma
455
17.1
Introduction
............................ 455
17.2
Block Coordinate Descent Methods
............... 461
17.3
Alternating Linearization Method
................ 469
17.4
Remarks on Numerical Performance
.............. 475
17.5
References
............................. 476
18
A Pathwise Algorithm for Covariance Selection
V. Krishnamurthy, S. D. Ahipayaoglu, and A. d Aspremont
479
18.1
Introduction
............................ 479
18.2
Covariance Selection
....................... 481
18.3
Algorithm
............................. 482
18.4
Numerical Results
........................ 487
18.5
Online Covariance Selection
................... 491
18.6
References
............................. 494
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| spelling | Optimization for machine learning ed. by Suvrit Sra ... Cambridge, Mass. [u.a.] MIT Press 2012 IX, 494 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Neural information processing series Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Mathematisches Modell (DE-588)4114528-8 gnd rswk-swf Optimierung (DE-588)4043664-0 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Maschinelles Lernen (DE-588)4193754-5 s Mathematisches Modell (DE-588)4114528-8 s Optimierung (DE-588)4043664-0 s DE-604 Sra, Suvrit 1976- (DE-588)1017466106 edt Digitalisierung UB Bamberg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025297201&sequence=000004&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Optimization for machine learning Maschinelles Lernen (DE-588)4193754-5 gnd Mathematisches Modell (DE-588)4114528-8 gnd Optimierung (DE-588)4043664-0 gnd |
| subject_GND | (DE-588)4193754-5 (DE-588)4114528-8 (DE-588)4043664-0 (DE-588)4143413-4 |
| title | Optimization for machine learning |
| title_auth | Optimization for machine learning |
| title_exact_search | Optimization for machine learning |
| title_full | Optimization for machine learning ed. by Suvrit Sra ... |
| title_fullStr | Optimization for machine learning ed. by Suvrit Sra ... |
| title_full_unstemmed | Optimization for machine learning ed. by Suvrit Sra ... |
| title_short | Optimization for machine learning |
| title_sort | optimization for machine learning |
| topic | Maschinelles Lernen (DE-588)4193754-5 gnd Mathematisches Modell (DE-588)4114528-8 gnd Optimierung (DE-588)4043664-0 gnd |
| topic_facet | Maschinelles Lernen Mathematisches Modell Optimierung Aufsatzsammlung |
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