Machine learning: theory and applications
Gespeichert in:
| Format: | Buch |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
Amsterdam [u.a.]
Elsevier
2013
|
| Ausgabe: | 1. ed. |
| Schriftenreihe: | Handbook of statistics
31 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XXIII, 525 S. Ill., graph. Darst. 24 cm |
| ISBN: | 9780444538598 |
Internformat
MARC
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| 245 | 1 | 0 | |a Machine learning |b theory and applications |c ed. by Venu Govindaraju ... |
| 250 | |a 1. ed. | ||
| 264 | 1 | |a Amsterdam [u.a.] |b Elsevier |c 2013 | |
| 300 | |a XXIII, 525 S. |b Ill., graph. Darst. |c 24 cm | ||
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Datensatz im Suchindex
| _version_ | 1804150432018202624 |
|---|---|
| adam_text | Table
of
Contents
Volume
31
Handbook of Statistics
Contributors: Vol.
31 xi
Preface to Handbook Volume
- 3 1 xv
Introduction
xvii
Part I: Theoretical Aspects
1
Ch.
1.
The Sequential Bootstrap
3
P.K. Patluik and C.R. Rao
1.
Introduction
4
2.
A sequential bootstrap resampling scheme
7
3.
Bootstrapping empirical measures with a random sample size
9
4.
Convergence rates lor the sequential bootstrap
12
5.
Second-order correctness of the sequential bootstrap
14
6.
Concluding remarks
17
Acknowledgments 1
7
References
18
Ch.
2.
The Cross-Entropy Method for Estimation
19
Dirk P. Kroesc,
Remen Y.
Rubinstein, and Peter W. Glynn
1.
Introduction
19
2.
Estimation setting
20
3.
Intensions
26
Acknowledgment
33
References
33
vj Tulile
of Contents
Ch.
3.
The Cross-Entropy Method for Optimization
35
y.dnivko
I.
Bolev, Dirk P. Kroese,
Renvoi Y.
Rubinstein,
atici
Pierre L Hai ver
1. Introduction
35
2.
I roni estimation to optimization
36
3.
Applications to combinatorial optimization
40
4.
Continuous optimization
50
5.
Summary
57
References
57
Ch.
4.
Probability Collectives in Optimization
61
David H. Wo/pert, Stefan R. Bicniawski, and Dev G. Rajnarayan
1
.
Introduction
6
1
2.
Delayed sampling theory
64
3.
Delayed sampling experiments
70
4.
Immediate .sampling theory
82
5.
Immediate sampling experiments
85
6.
Conclusion
97
References
98
Ch.
5.
Bagging, Boosting, and Random Forests Using
R
101
Hansen Baiinerman-Thompson,
M.
Bhaskara
Rao,
and Subramanyam
Kasala
1.
Introduction
101
2.
Data sets and rationale
103
3.
Bagging
107
4.
Boosting
113
5.
Do Bagging and Boosting really work?
115
6.
What is a classification tree?
116
7.
Classification tree versus logistic regression
126
8.
Random forest
127
9.
Random forest, genetics, and cross-validation
134
10.
Regression trees
139
1
1.
Boosting using the
R
package,
ada
144
12.
Epilog
149
References
149
Ch.
6.
Matching Score Fusion Methods
151
Sergey Tulyakov and
Venu Govindaraju
1.
Introduction
151
2.
Matching systems
156
3.
Selected approaches to fusion in matching systems
158
4.
Operating modes of matching systems
162
5.
Complexity types of classifier combination methods
163
6.
Modeling matching score dependencies
165
7.
Score combination applications
167
8.
Conclusion
169
Appendices
169
Tahle
f
Contents
л.
Proof of Claim
4
169
в.
Proof of Claim
5
171
References
173
Part II: Object Recognition
177
Ch.
7.
Statistical Methods on Special Manifolds for Image and Video
Understanding
179
Paran
Turagli, Rama Che/lappa, and
Anu/
Srivastavci
1.
Introduction
179
2.
Some
motivating examples 1
80
3.
Differential geometric tools
181
4.
Common manifolds arising in image analysis
185
5.
Applications in image analysis
187
6.
Summary and discussion
198
Acknowledgments
198
References
198
Ch.
8.
Dictionary-Based Methods for Object Recognition
203
Vishal M.
Patel
and Rama Chellappa
1.
Introduction
203
2.
Sparse representation
204
3.
Dictionary learning
210
4.
Concluding remarks
222
References
222
Ch.
9.
Conditional Random Fields for Scene Labeling
227
¡fcoma Nwogu and
Venu
Govindaraju
1.
Introduction
227
2.
Overview of
CRI
229
3.
Scene parsing
236
4.
More recent implementations of CRF scene labelings
242
5.
Conclusion and future directions
245
References
245
Ch.
10.
Shape-Based Image Classification and Retrieval
249
N.
Mohaiav, A. Lee-St. John, R. Manmatha,
and T.M. Rath
1.
Introduction
250
2.
Prior work
251
3.
Classification and retrieval models
252
4.
Features
257
5.
Classification experiments
261
6.
Retrieval
262
vj¡¡
Tahle
of Contents
7.
Multiple class labels
265
8.
Summary and conclusions
266
References
266
Ch.
11.
Visual Search: A Large-Scale Perspective
269
Robinson Pirainutlm, Anurag Bhardwaj, Wei
Di,
and Nee/
Sundaresan
1.
Introduction
269
2.
When is big data important?
272
3.
Information extraction and representation
273
4.
Matching images 2H2
5.
Practical considerations: memory footprint and speed
2X5
6.
Benchmark data sets
289
7.
Closing remarks
291
References
293
Part III:
Biometrie
Systems
299
Ch.
12.
Video Activity Recognition by Luminance Differential Trajectory and Aligned
Projection Distance
301
Haomian Zheng, Zhu
Li, Yun Fu,
Agge/os K.
Ka/sagge/os, and Jane You
1.
Introduction
302
2.
Related work
303
3.
Problem formulation
305
4.
DLFT and LAPD solutions
312
5.
Experiments
314
6.
Conclusion
323
References
324
Ch.
13.
Soft Biometrics for Surveillance: An Overview
327
D.A. Reid, S. Samangooei, C. Chen, M.S. Nixon, and A. Ross
1.
Introduction
327
2.
Performance metrics
329
3.
Incorporating soft biometrics in a fusion framework
330
4.
Human identification using soft biometrics
333
5.
Predicting gender from face images
344
6.
Applications
346
7.
Conclusion
349
References
350
Ch.
14.
A User Behavior Monitoring and Profiling Scheme for Masquerade
Detection
353
Ashish Garg, Shambhu Upadhyaya, and Kevin
Kwiat
1.
Introduction
354
2.
Related work
356
3.
Support Vector Machines (SVMs)
358
Tuhle
of Contents
4.
Data collection, feature extraction, and feature vector generation
361
5.
Kxperimental design
367
6.
Discussion and conclusion
376
Acknowledgments
376
References
377
Ch.
15.
Application of Bayesian Graphical Models to Iris Recognition
381
B.V.K. Vijciya Kumar, Vishnu Narcsh Boddeti, Jonathan M. Smereka,
Jctson Thornton, anil Marias Savvidcs
1. Introduction
3X1
2.
Gabor wavelet-based matching
3X3
3.
Correlation filter-based iris matching
3X6
4.
Bayesian graphical model for iris recognition
390
5.
Summary
397
Acknowledgments
397
References
397
Part IV: Document Analysis
399
Ch,
16.
Learning Algorithms for Document Layout Analysis
401
Simone
Marinai
1.
Introduction
401
2.
Pixel classification
405
3.
Zone classification
406
4.
Connected component classification
409
5.
Text region segmentation
411
6.
Region classification
412
7.
Functional labeling
415
8.
Conclusion
416
References
416
Ch.
17,
Hidden Markov Models for Off-Line Cursive Handwriting
Recognition
421
Andreas Fischer,
Volk
mar Frinken, and
Horst
Bunke
1.
Introduction
421
2.
Serialization of handwriting images
423
3.
HMM-based text line recognition
426
4.
Outlook and conclusions
438
Acknowledgment
439
References
439
Ch.
18.
Machine Learning in Handwritten Arabic Text Recognition
443
Utkarsh
Porwał,
Zhixin Shi, and Srirangaraj Setlur
1.
Introduction
443
2.
Arabic script challenges for recognition
444
3.
Learning paradigms
447
4.
Features for text recognition
455
x
Table of Contents
5.
Models for recognition
460
6.
Conclusion
467
References
468
Ch.
19.
Manifold Learning for the Shape-Based Recognition of Historical
Arabic Documents
471
Mohamed Cheriéi,
Reza
Farrahi Moghaddani, E/isan Arahnejad,
and Guoqiang /.hong
1.
Introduction
471
2
Problem statement
475
3.
Manifold learning
475
4.
Feature extraction
480
5.
Experimental
results
481
6.
Conclusion and future
prospects
Acknowledgments
489
References
489
488
Ch.
20.
Query Suggestion with Large Scale Data
493
Nish Parikh, Gyanit Singh, and Ned
Sundaresan
1.
Introduction
493
2.
Terminology
499
3.
Approaches to generation of Query Suggestions
501
4.
Evaluation methods of QS
508
5.
Properties of large scale data
509
6.
Query Suggestion in practice
513
7.
Closing remarks
515
References
516
Subject Index
519
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| id | DE-604.BV041065565 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T00:38:49Z |
| institution | BVB |
| isbn | 9780444538598 |
| language | English |
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| spelling | Machine learning theory and applications ed. by Venu Govindaraju ... 1. ed. Amsterdam [u.a.] Elsevier 2013 XXIII, 525 S. Ill., graph. Darst. 24 cm txt rdacontent n rdamedia nc rdacarrier Handbook of statistics 31 Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 s DE-604 Govindaraju, Venu 1964- Sonstige (DE-588)1036336638 oth Handbook of statistics 31 (DE-604)BV000002510 31 Digitalisierung UB Bamberg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026042595&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Machine learning theory and applications Handbook of statistics Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4193754-5 |
| title | Machine learning theory and applications |
| title_auth | Machine learning theory and applications |
| title_exact_search | Machine learning theory and applications |
| title_full | Machine learning theory and applications ed. by Venu Govindaraju ... |
| title_fullStr | Machine learning theory and applications ed. by Venu Govindaraju ... |
| title_full_unstemmed | Machine learning theory and applications ed. by Venu Govindaraju ... |
| title_short | Machine learning |
| title_sort | machine learning theory and applications |
| title_sub | theory and applications |
| topic | Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Maschinelles Lernen |
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