Artificial neural networks for speech and vision:
Gespeichert in:
| Format: | Buch |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
London [u.a.]
Chapman & Hall
1994
|
| Ausgabe: | 1. ed. |
| Schriftenreihe: | Chapman & Hall neural computing series
4 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XX, 586 S. graph. Darst. |
| ISBN: | 041254850X |
Internformat
MARC
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| 245 | 1 | 0 | |a Artificial neural networks for speech and vision |c ed. by Richard J. Mammone |
| 250 | |a 1. ed. | ||
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Datensatz im Suchindex
| _version_ | 1804123396129161216 |
|---|---|
| adam_text | Artificial Neural Networks for
Speech and Vision
Edited by
Richard J Mammone
Professor of Electrical and Computer Engineering
Rutgers University
New Jersey, USA
CHAPMAN amp; HALL
London • Glasgow • New York • Tokyo • Melbourne • Madras
Contents
Contributors xiii
Preface xvii
Acknowledgements xxi
PART ONE ARTIFICIAL NEURAL NETWORKS (ANN)
1 Artificial neural networks: a decade of progress 3
David J Burr
1 1 Introduction 3
1 2 Multi-layer Perceptrons and learning 4
1 3 Recurrent and time delay networks 4
1 4 HMMs and hybrid systems 5
1 5 Elastic and feature mapping networks 5
1 6 MLP applications 6
1 7 Summary 7
References 7
2 Continuous computing: thoughts on Turing equivalence,
programming and noise 11
Steven C Suddarth
2 1 Introduction 11
2 2 Time and neuromorphic processing 12
2 3 Processing with dynamical systems 12
2 4 Programming 13
2 5 Noise 15
2 6 Storing programs 16
2 7 Summary 18
References 18
3 Statistical physics algorithms that converge 19
A L Yuille and J J Kosowsky
3 1 Statistical physics and mean field theory for optimization 19
i CONTENTS
3 2 The saddle point approximation 20
3 3 Related algorithms 22
3 4 Bounds on convergence 28
3 5 The linear assignment problem 29
3 6 Conclusion 32
Appendix 3A: Proofs 33
References 34
4 Noisy linear networks 37
Kurt Hornik
4 1 Introduction 37
4 2 Linear feature extraction contaminated by noise 38
Appendix 4A: Mathematical proofs 42
References 43
5 Short-term memory structures for dynamic neural networks 45
Bert de Vries
5 1 Introduction 45
5 2 Memory filters 46
5 3 Dealing with temporal variation 50
5 4 Conclusions 52
References 53
6 The effect of higher order in recurrent neural networks:
experiments 54
C Lee Giles and Clifford B Miller
6 1 Introduction 54
6 2 Dynamic recurrent neural networks 56
6 3 Training recurrent neural networks to infer grammars 64
6 4 Results 66
6 5 Conclusions 75
References 76
7 Receptive field partitioning for wavelet networks 79
Toufic I Boubez ,
7 1 Introduction 79
7 2 Feed-forward neural networks 80
7 3 Biological sensory receptors 84
7 4 The wavelet transform 85
7 5 Wavelet neural networks (WNN) 89
7 6 Results 92
References 95
CONTENTS vii
8 Rates of convergence for radial basis functions and
neural networks 97
Federico Girosi and Gabriele Anzellotti
8 1 Introduction 97
8 2 Rates of convergence 100
8 3 The Maurey-Jones-Barron lemma 101
8 4 Approximation by translates of a function G 102
8 5 Examples of functions G 105
8 6 Other approximation schemes 107
8 7 Conclusions 109
Appendix 8A: The Bochner integral 110
Appendix 8B: Sobolev spaces and the space A 111
References 113
9 Uniqueness of weights for neural networks 115
Francesco Albertini, Eduardo D Sontag and Vincent Maillot
9 1 Introduction 115
9 2 Single-hidden layer nets 116
9 3 Recurrent nets 122
References 125
10 When networks disagree: Ensemble methods for
hybrid neural networks 126
Michael P Perrone and Leon N Cooper
10 1 Introduction 126
10 2 Basic Ensemble Method 127
10 3 Intuitive illustrations 130
10 4 Generalized Ensemble Method 133
10 5 Experimental results 134
10 6 Improving BEM and GEM 139
10 7 Conclusions 140
References 141
11 Non-literal transfer among neural network learners 143
LY Pratt
11 1 Introduction 143
11 2 Motivation and related work 144
11 3 Discriminability-Based Transfer (DBT) algorithm 147
11 4 Training methodology 157
11 5 Results 159
11 6 Discussion 161
11 7 Conclusion 165
References 166
viii CONTENTS
12 Long-term memory for neural networks 170
Anshu Agarwal and Richard J Mammone
12 1 Introduction 170
12 2 Earlier approaches to the eliminating interference 172
12 3 Hyperplane perturbation 175
12 4 The row action projection (RAP) algorithm 176
12 5 Hyperplane perturbation using RAP 179
12 6 Application of new algorithm to NTN 181
12 7 Simulation results 185
12 8 Conclusions and further work 190
References 191
13 The Relabeling Exchange Method (REM) for
training neural networks 194
Wen Wu and Richard J Mammone
13 1 Introduction 194
13 2 Relabeling for supervised learning 197
13 3 Relabeling algorithms and simulations 203
13 4 Numerical results 205
13 5 Summary 208
References 208
14 Learning by learning in neural networks 210
D K Naik and Richard J Mammone
14 1 Introduction 210
14 2 Learning by learning with neural networks 211
14 3 The meta-network approach 213
14 4 Numerical simulations 219
14 5 Conclusion and future work 223
References 224
PART TWO ANN APPLICATIONS IN SPEECH AND
LANGUAGE UNDERSTANDING
15 A self-learning neural tree network for phone recognition 227
Mazin G Rahim
15 1 Introduction 227
15 2 SL-NTN: Self-Learning Neural Tree Network 228
15 3 Experimental results 233
15 4 Summary 238
References 238
CONTENTS ix
16 Some relationships between ANNs and HMMs 240
Arthur Nddas
Introduction
HMMs that are defined by ANNs
ANNs that are defined by HMMs
Appendix 16A: Alternative parametrizations
and constraints in HMMC
References
Comparison of feed-forward and recurrent sensitivities in
speech recognition
Gary
M Kuhn and Raymond L Watrous
Introduction
The networks
Feed-forward sensitivity
Feature optimizing
Forward calculation of recurrent sensitivity
Backward calculation of recurrent sensitivity
Simulations
Discussion
Conclusion
References
18 Discriminative feature extraction 278
Shigeru Katagiri, Biing-Hwang Juang and Alain Biem
18 1 Introduction 278
18 2 MCE/GPD-based discriminative learning 279
18 3 Task formalization using smooth functions 286
18 4 Discriminative feature extraction for speech recognition 287
18 5 Summary 291
References 291
19 Word reading in damaged connectionist networks:
computational and neuropsychological implications 294
David C Plaut and Tim Shallice
19 1 Introduction 294
19?2 The testing procedure 299
19 3 The network architecture 310
19 4 The training procedure 312
19 5 The task domain 319
19 6 Conclusions 320
References 321
20 Adaptive language acquisition in a multi-sensory device
Ananth Sankar and Allen Gorin
20 1 Introduction
20 2 Background
20 3 A feedback mechanism for visual focus of attention
20 4 Network structure
20 5 Conversation segmentation
20 6 Experimental results
20 7 Summary and future work
References
CONTENTS
21 Algebraic learning of statistical associations for
language acquisition 357
Allen Gorin and Naftali Tishby
21 1 Introduction 357
21 2 Calculus of associations 362
21 3 Semantic supervision as equations in associations 373
21 4 Focused learning for language acquisition 380
21 5 Conclusions 384
References 385
22 Incremental algebraic learning for adaptive language acquisition 388
K R Farrell, Richard J Mammone and Allen L Gorin
22 1 Introduction 388
22 2 Adaptive language acquisition 389
22 3 Motivation 390
22 4 Problem formulation 392
22 5 Incremental methods 393
22 6 Experimental results 395
22 7 Conclusions and future work 399
References 400
23 Adaptive language acquisition for an airline information
subsystem 401
A N Gertner and Allen L Gorin
23 1 Introduction 401
23 2 Task structure 407
23 3 Language structure 408
23 4 A network with an intermediate layer of nonterminals 409
23 5 Experimental evaluation 418
23 6 Conclusions 425
References 426
CONTENTS xi
PART THREE ANN APPLICATIONS IN VISION
24 A miniaturized space-variant active vision system: Cortex-1 429
Benjamin B Bederson, Richard S Wallace and Eric L Schwartz
24 1 Introduction 429
24 2 Space-variant images 431
24 3 System description 437
24 4 Programming and development model 447
24 5 Applications 452
24 6 Conclusion 454
References 455
25 A biologically based synthetic nervous system for
a real-world device 457
George N Reeke, Jr , OlafSporns, W Einar Gall, Giulio Tononi
and Gerald M Edelman
25 1 Introduction 457
25 2 Materials and methods 461
25 3 Results 466
25 4 Discussion 470
References 472
26 Dynamic formation and reset of coherent visual
segmentations by neural networks 474
Gregory Francis, Stephen Grossberg and Ennio Mingolla
26 1 Introduction 474
26 2 Feature binding by the boundary contour system 475
26 3 Feature binding as a source of visual persistence 484
26 4 Control of smearing due to moving images 487
26 5 Gated dipole: production of a reset signal 489
26 6 Persistence of illusory contours 495
26 7 Conclusions 498
References 499
27 Recognition of space-time gestures using a distributed
representation 502
Trevor J Darrell and Alex P Pentland
it 1 Introduction 502
27 2 Learning view models 505
27 3 Using temporal context to guide search 508
27 4 Non-rigid objects and sparse view spaces 510
27 5 Training gestures 511
27 6 A gesture recognition example 513
27 7 Implementation issues and real-time performance 513
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| spelling | Artificial neural networks for speech and vision ed. by Richard J. Mammone 1. ed. London [u.a.] Chapman & Hall 1994 XX, 586 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Chapman & Hall neural computing series 4 Neuronales Netz (DE-588)4226127-2 gnd rswk-swf Digitale Sprachverarbeitung (DE-588)4233857-8 gnd rswk-swf Bildverarbeitung (DE-588)4006684-8 gnd rswk-swf Bildverarbeitung (DE-588)4006684-8 s Neuronales Netz (DE-588)4226127-2 s DE-604 Digitale Sprachverarbeitung (DE-588)4233857-8 s Mammone, Richard J. Sonstige oth Chapman & Hall neural computing series 4 (DE-604)BV007221245 4 HEBIS Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=005982688&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
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| title | Artificial neural networks for speech and vision |
| title_auth | Artificial neural networks for speech and vision |
| title_exact_search | Artificial neural networks for speech and vision |
| title_full | Artificial neural networks for speech and vision ed. by Richard J. Mammone |
| title_fullStr | Artificial neural networks for speech and vision ed. by Richard J. Mammone |
| title_full_unstemmed | Artificial neural networks for speech and vision ed. by Richard J. Mammone |
| title_short | Artificial neural networks for speech and vision |
| title_sort | artificial neural networks for speech and vision |
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