Scaling up machine learning: parallel and distributed approaches
Saved in:
| Format: | Book |
|---|---|
| Language: | English |
| Published: |
Cambridge [u.a.]
Cambridge Univ.Press
2012
|
| Edition: | 1. publ. |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Hier auch später erschienene, unveränderte Nachdrucke |
| Physical Description: | XVI, 475 S. Ill., graph. Darst. |
| ISBN: | 9780521192248 |
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| 245 | 1 | 0 | |a Scaling up machine learning |b parallel and distributed approaches |c ed. by Ron Bekkerman ... |
| 250 | |a 1. publ. | ||
| 264 | 1 | |a Cambridge [u.a.] |b Cambridge Univ.Press |c 2012 | |
| 300 | |a XVI, 475 S. |b Ill., graph. Darst. | ||
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Record in the Search Index
| _version_ | 1804148642865479680 |
|---|---|
| adam_text | Contents
Contributors
xi
Preface
xv
1
Scaling Up Machine Learning: Introduction
1
Ron Bekkerman, Mikhail Bilenko, and John
Langford
1.1
Machine Learning Basics
2
1.2
Reasons for Scaling Up Machine Learning
3
1.3
Key Concepts in Parallel and Distributed Computing
6
1.4
Platform Choices and Trade-Offs
7
1.5
Thinking about Performance
9
1.6
Organization of the Book
10
1.7
Bibliographic Notes
17
References
19
Part One Frameworks for Scaling Up Machine Learning
2
MapReduce and Its Application to Massively Parallel Learning
of Decision Tree Ensembles
23
Biswanath Panda, Joshua S.
Herbach,
Sugato
Basu,
and Roberto
J.
Bayardo
2.1
Preliminaries
24
2.2
Example of PLANET
30
2.3
Technical Details
33
2.4
Learning Ensembles
38
2.5
Engineering Issues
39
2.6
Experiments
41
2.7
Related Work
44
2.8
Conclusions
46
Acknowledgments
47
References
47
3
Large-Scale Machine
Learning Using DryadLINQ
49
Mihai Budiu, Dennis Fetterly, Michael
Isard,
Frank McSherry, and Yuan Yu
3.1
Manipulating
Datasets
with LINQ
49
3.2
/fe-Means in LINQ
52
3.3
Running LINQ on a Cluster with DryadLINQ
53
3.4
Lessons Learned
65
References
67
4
IBM Parallel Machine Learning Toolbox
69
Edwin Pednault,
Elad
Yom-Tov, and Amol Ghoting
4.1
Data-Parallel Associative-Commutative Computation
70
4.2
API and Control Layer
71
4.3
API Extensions for Distributed-State Algorithms
76
4.4
Control Layer Implementation and Optimizations
77
4.5
Parallel Kernel ¿-Means
79
4.6
Parallel Decision Tree
80
4.7
Parallel Frequent Pattern Mining
83
4.8
Summary
86
References
87
5
Uniformly Fine-Grained Data-Parallel Computing for Machine
Learning Algorithms
89
Meichun Hsu, Ren Wu, and Bin Zhang
5.1
Overview of a GP-GPU
91
5.2
Uniformly Fine-Grained Data-Parallel Computing on a GPU
93
5.3
The
ł-Means
Clustering Algorithm
97
5.4
The
ł-Means
Regression Clustering Algorithm
99
5.5
Implementations and Performance Comparisons
102
5.6
Conclusions
105
References
105
Part Two Supervised and Unsupervised Learning Algorithms
6
PSVM: Parallel Support Vector Machines with Incomplete
Cholesky Factorization
109
Edward Y. Chang, Hongjie
Bai, Kaihua
Zhu, Hao Wang,
Jian Li,
and Zhihuan Qiu
6.1
Interior Point Method with Incomplete Cholesky Factorization
112
6.2
PSVM Algorithm
114
6.3
Experiments
121
6.4
Conclusion
125
Acknowledgments
125
References
125
7
Massive SVM Parallelization Using Hardware Accelerators
127
Igor Durdanovic, Eric Cosatto, Hans Peter Graf, Srihari Cadambi,
Venkata Jakkula, Srimat Chakradhar, and Abhinandan Majumdar
7.1
Problem Formulation
128
7.2
Implementation of the
SMO
Algorithm
131
7.3
Micro Parallelization:
Related Work
132
7.4
Previous Parallelizations on Multicore Systems
133
7.5
Micro Parallelization: Revisited
136
7.6
Massively Parallel Hardware Accelerator
137
7.7
Results
145
7.8
Conclusion
146
References
146
8
Large-Scale Learning to Rank Using Boosted Decision Trees
148
Krysta
M.
Svore
and Christopher
J.
C. Burges
8.1
Related Work
149
8.2
LambdaMART
151
8.3
Approaches to Distributing LambdaMART
153
8.4
Experiments
158
8.5
Conclusions and Future Work
168
8.6
Acknowledgments
169
References
169
9
The Transform Regression Algorithm
170
Ramesh Natarajan and Edwin Pednault
9.1
Classification, Regression, and Loss Functions
171
9.2
Background
172
9.3
Motivation and Algorithm Description
173
9.4
TReg Expansion: Initialization and Termination
177
9.5
Model Accuracy Results
184
9.6
Parallel Performance Results
186
9.7
Summary
188
References
189
10
Parallel Belief Propagation in Factor Graphs
190
Joseph Gonzalez, Yucheng Low, and Carlos Guestrin
10.1
Belief Propagation in Factor Graphs
191
10.2
Shared Memory Parallel Belief Propagation
195
10.3
Multicore Performance Comparison
209
10.4
Parallel Belief Propagation on Clusters
210
10.5
Conclusion
214
Acknowledgments
214
References
214
11
Distributed Gibbs Sampling for Latent Variable Models
217
Arthur Asuncion, Padhraic Smyth, Max Welling, David Newman,
Ian Porteous, and Scott
Triglia
11.1
Latent Variable Models
217
11.2
Distributed Inference Algorithms
220
11.3
Experimental Analysis of Distributed Topic Modeling
224
11.4
Practical Guidelines for Implementation
229
11.5
A Foray into Distributed Inference for Bayesian Networks
231
11.6
Conclusion
236
Acknowledgments
237
References
237
12
Large-Scale Spectral Clustering with MapReduce and
MPI
240
Wen-Yen Chen, Yangqiu Song, Hongjie
Bai, Chih-Jen
Lin,
and Edward Y. Chang
12.1
Spectral Clustering
241
12.2
Spectral Clustering Using a Sparse Similarity Matrix
243
12.3
Parallel Spectral Clustering (PSC) Using a Sparse Similarity Matrix
245
12.4
Experiments
251
12.5
Conclusions
258
References
259
13
Parallelizing Information-Theoretic Clustering Methods
262
Ron Bekkerman and Martin
Scholz
13.1
Information-Theoretic Clustering
264
13.2
Parallel Clustering
266
13.3
Sequential Co-clustering
269
13.4
The DataLoom Algorithm
270
13.5
Implementation and Experimentation
274
13.6
Conclusion
277
References
278
Part Three Alternative Learning Settings
14
Parallel Online Learning
283
Daniel Hsu, Nikos Karampatziakis, John
Langford,
and Alex
J. Smola
14.1
Limits Due to Bandwidth and Latency
285
14.2
Parallelization Strategies
286
14.3
Delayed Update Analysis
288
14.4
Parallel Learning Algorithms
290
14.5
Global Update Rules
298
14.6
Experiments
302
14.7
Conclusion
303
References
305
15
Parallel Graph-Based Semi-Supervised Learning
307
JeffBilmes and Amarnag Subramanya
15.1
Scaling SSL to Large
Datasets
309
15.2
Graph-Based SSL
310
15.3
Dataset:
A ^O-Million-Node Graph
317
15.4
Large-Scale Parallel Processing
319
15.5
Discussion
327
References
328
16
Distributed Transfer Learning via Cooperative Matrix
Factorization
331
Evan Xiang, Nathan Liu, and Qiang Yang
16.1
Distributed Coalitional Learning
333
16.2
Extension of DisCo to Classification Tasks
343
16.3
Conclusion
350
References
350
17
Parallel Large-Scale Feature Selection
352
Jeremy Kubica, Sameer Singh, and
Daria
Sorokina
17.1
Logistic Regression
353
17.2
Feature Selection
354
17.3
Parallelizing Feature Selection Algorithms
358
17.4
Experimental Results
363
17.5
Conclusions
368
References
368
Part Four Applications
18
Large-Scale Learning for Vision with GPUs
373
Adam
Coates,
Rajat Raina,
and Andrew Y. Ng
18.1
A Standard Pipeline
374
18.2
Introduction to GPUs
377
18.3
A Standard Approach Scaled Up
380
18.4
Feature Learning with Deep Belief Networks
388
18.5
Conclusion
395
References
395
19
Large-Scale FPGA-Based Convolutional Networks
399
Clément
Farabet,
Yann
LeCun, Koray Kavukcuoglu,
Berin
Martini,
Polina Akselrod,
Selcuk Talay,
and
Eugenio Culurciello
19.1
Learning Internal Representations
400
19.2
A Dedicated Digital Hardware Architecture
405
19.3
Summary
416
References
417
20
Mining Tree-Structured Data on Multicore Systems
420
Shirish Tatikonda and Srinivasan Parthasarathy
20.1
The Multicore Challenge
422
20.2
Background
423
20.3
Memory Optimizations
427
20.4
Adaptive Parallelization
431
20.5
Empirical Evaluation
437
20.6
Discussion
442
Acknowledgments
443
References
443
21
Scalable Parallelization of Automatic Speech Recognition
446
Jike Chong, Ekaterina Gonina, Kisun You, and Kurt Keutzer
21.1
Concurrency Identification
450
21.2
Software Architecture and Implementation Challenges
452
21.3
Multicore and Manycore Parallel Platforms
454
21.4
Multicore Infrastructure and Mapping
455
X
CONTENTS
21.5 The Manycore Implementation 459
21.6 Implementation
Profiling and Sensitivity Analysis
462
21.7
Application-Level Optimization
464
21.8
Conclusion and Key Lessons
467
References
468
Subject Index
471
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| spelling | Scaling up machine learning parallel and distributed approaches ed. by Ron Bekkerman ... 1. publ. Cambridge [u.a.] Cambridge Univ.Press 2012 XVI, 475 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Hier auch später erschienene, unveränderte Nachdrucke Machine learning Data mining Parallel algorithms Parallel programs (Computer programs) COMPUTERS / Computer Vision & Pattern Recognition bisacsh Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 s DE-604 Bekkerman, Ron Sonstige (DE-588)1029441243 oth Digitalisierung UB Passau application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024598298&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
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| subject_GND | (DE-588)4193754-5 |
| title | Scaling up machine learning parallel and distributed approaches |
| title_auth | Scaling up machine learning parallel and distributed approaches |
| title_exact_search | Scaling up machine learning parallel and distributed approaches |
| title_full | Scaling up machine learning parallel and distributed approaches ed. by Ron Bekkerman ... |
| title_fullStr | Scaling up machine learning parallel and distributed approaches ed. by Ron Bekkerman ... |
| title_full_unstemmed | Scaling up machine learning parallel and distributed approaches ed. by Ron Bekkerman ... |
| title_short | Scaling up machine learning |
| title_sort | scaling up machine learning parallel and distributed approaches |
| title_sub | parallel and distributed approaches |
| topic | Machine learning Data mining Parallel algorithms Parallel programs (Computer programs) COMPUTERS / Computer Vision & Pattern Recognition bisacsh Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Machine learning Data mining Parallel algorithms Parallel programs (Computer programs) COMPUTERS / Computer Vision & Pattern Recognition Maschinelles Lernen |
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