رسالة دكتوراه بعنوان Learning, Cooperation and Feedback in Pattern Recognition
منتدى هندسة الإنتاج والتصميم الميكانيكى
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منتدى هندسة الإنتاج والتصميم الميكانيكى
بسم الله الرحمن الرحيم

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 رسالة دكتوراه بعنوان Learning, Cooperation and Feedback in Pattern Recognition

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كاتب الموضوعرسالة
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عدد المساهمات : 17736
التقييم : 32016
تاريخ التسجيل : 01/07/2009
الدولة : مصر
العمل : مدير منتدى هندسة الإنتاج والتصميم الميكانيكى

رسالة دكتوراه بعنوان Learning, Cooperation and Feedback in Pattern Recognition  Empty
مُساهمةموضوع: رسالة دكتوراه بعنوان Learning, Cooperation and Feedback in Pattern Recognition    رسالة دكتوراه بعنوان Learning, Cooperation and Feedback in Pattern Recognition  Emptyالسبت 27 أغسطس 2022, 3:31 am

أخواني في الله
أحضرت لكم
رسالة دكتوراه بعنوان
Learning, Cooperation and Feedback in Pattern Recognition
Malcolm J. A. Strens
Defence Evaluation & Research Agency
and
King’s College London
Ph.D. Thesis

رسالة دكتوراه بعنوان Learning, Cooperation and Feedback in Pattern Recognition  L_c_a_10
و المحتوى كما يلي :


Contents
1 INTRODUCTION .15
1.1 Reinforcement learning for acquiring visual competence .16
1.2 Overview 17
1.3 Theory 19
1.4 Summary by chapter .20
PART I: MARKOV DECISION PROCESS MODELS FOR THE AGENT
ENVIRONMENT INTERFACE .23
2 MODELLING THE AGENT ENVIRONMENT INTERFACE .25
2.1 Introduction 25
2.2 Markov system definition .25
2.3 Markov decision process definition 26
2.4 The agent environment interface 27
2.5 MDP model for agent reasoning .34
2.6 Visibility of the MDP to the agent 35
2.7 Value functions for Markov decision processes .35
2.8 Dynamic programming .36
2.9 Dynamic programming for value function estimation 37
2.10 Summary 39
3 REINFORCEMENT LEARNING .41
3.1 Introduction 41
3.2 Reinforcement learning process: formal definition 41
3.3 A Markov system prediction problem 42
3.4 Actor-critic reinforcement learning 47
3.5 Q-learning and Sarsa 49
3.6 Summary 56
4 DYNAMIC PROGRAMMING WITH ON-LINE MODELS 57
4.1 Introduction 57
4.2 Estimating reward models on line 57
4.3 Estimating transition probabilities on-line 58
4.4 Storing sparse data in hash tables .59
4.5 Dynamic programming with on-line models 60
4.6 Dyna .61
4.7 Prioritised Sweep and Queue-Dyna 62
4.8 Global propagation .63
4.9 Feedback loops in the MDP 64
4.10 Experimental comparison 67
4.11 Analysis of experimental results 71
4.12 Conclusions from experimental results 71
4.13 Summary 726
PART II: REPRESENTATIONS IN LEARNING . 73
5 UNSUPERVISED LEARNING . 75
5.1 Introduction 75
5.2 Motivation 75
5.3 Unsupervised learning process: formal definition 76
5.4 Goals for Unsupervised Learning . 76
5.5 The Input Process . 84
5.6 Summary 86
6 UNSUPERVISED LEARNING TECHNIQUES 87
6.1 Introduction 87
6.2 Techniques for redundancy reduction and minimum error reconstruction . 87
6.3 Techniques for novelty detection . 90
6.4 Competitive (anti-Hebbian) learning 91
6.5 Techniques for generalisation & discrimination . 93
6.6 Summary 94
7 STATE REPRESENTATION, LEARNING AND STABILITY 97
7.1 Introduction 97
7.2 Sampling a continuous state space . 98
7.3 Representing state in multiple sub-spaces 99
7.4 Generalisation in state representation . 100
7.5 Partially compressed codes 101
7.6 States as clusters in unsupervised learning . 102
7.7 Feedback in state learning 103
7.8 The state foundation problem . 104
7.9 The action foundation problem 108
7.10 The reward foundation problem 108
7.11 Summary . 109
8 ADAPTIVE RESONANCE THEORY CLUSTERING ALGORITHMS 111
8.1 Introduction 111
8.2 Magnitude-dependent distance measures . 111
8.3 The Stability-Plasticity Dilemma . 114
8.4 Adaptive Resonance Theory 114
8.5 ART1 algorithm . 114
8.6 Fuzzy ART algorithm . 117
8.7 Interpretation of fuzzy ART as ART1 applied to stack representation . 117
8.8 Information-theoretic considerations 119
8.9 Category proliferation 119
8.10 Modifying learning in fuzzy ART to inhibit category proliferation 120
8.11 ART2-A . 123
8.12 Other ART algorithms . 124
8.13 Summary . 125
9 ACTION REPRESENTATION AND LEARNING 127
9.1 Introduction 127
9.2 Action learning by correlation 128
9.3 Action learning by correlation in animals 131
9.4 Hierarchies of control . 132
9.5 Summary 1337
PART III: CASE STUDIES 135
10 LEARNING TO DETECT (CASE STUDY 1) 137
10.1 Introduction .137
10.2 A target detection process 137
10.3 Problem discussion 138
10.4 Synthetic data and synthetic environments 139
10.5 Potential modes of operation .140
10.6 Objectives of experiments .141
10.7 Configuration of experiments 142
10.8 State measurements .143
10.9 State estimation module .144
10.10 Action translation module 145
10.11 Reward calculation module .146
10.12 Trials 148
10.13 Training set description .149
10.14 Experimental procedure .151
10.15 Validation set performance 151
10.16 Test set performance 153
10.17 Conclusions from experimental results 154
10.18 Summary 155
11 MULTI-SCALE IMAGE REPRESENTATION (CASE STUDY 2) 157
11.1 Introduction .157
11.2 Requirements for the representation 157
11.3 Colour, motion, and other visual cues 159
11.4 Image filtering by convolution 159
11.5 Fast Fourier Transform for image convolution 160
11.6 Difference-of-Gaussian filters .161
11.7 Frequency domain image subsampling 162
11.8 Fractional scales 162
11.9 Multi-scale DoG stack .163
11.10 On-channel representation .166
11.11 Summary 167
12 ATTENTIONAL MECHANISMS FOR INVARIANT FEATURE EXTRACTION (CASE STUDY 2) 169
12.1 Introduction .169
12.2 Visual attention 170
12.3 Geometrical invariance 172
12.4 Foveation .174
12.5 Stack indexing and trilinear interpolation 175
12.6 Scale estimation .176
12.7 Stack calibration 179
12.8 Scale estimation accuracy 179
12.9 Orientation estimation .181
12.10 Feature image foveation and coding 186
12.11 Contrast invariance 187
12.12 Summary 187
13 LEARNING TO RECOGNISE (CASE STUDY 2) 189
13.1 Introduction .189
13.2 State estimation 190
13.3 Clustering algorithm 191
13.4 Feedback between RL agent and state estimation sub-systems .191
13.5 Reward calculation: supervised learning phase .192
13.6 Actions .196
13.7 Experimental regime 197
13.8 Training set description .199
13.9 Exploitation phase .200
13.10 Summary 201
14 EXPERIMENTS WITH THE RECOGNITION SYSTEM (CASE STUDY 2) .203
14.1 Introduction .203
14.2 Performance results .203
14.3 States self-organised by the system 206
14.4 Trajectory analysis .207
14.5 Testing the requirement for feedback 209
14.6 Behaviour in the presence of novel input .210
14.7 Conclusions from experimental results 211
14.8 Summary 2128
15 CONCLUSIONS 213
15.1 Introduction . 213
15.2 Requirements for learning states, actions and rewards 213
15.3 Acquiring visual competence 216
15.4 Implementation considerations 219
15.5 Unsupervised learning as a state estimation technique: discussion . 220
15.6 The control of visual processing: discussion . 221
15.7 Hierarchical visual processing . 222
15.8 Summary . 223
APPENDIX A: REFERENCE LIST 225
APPENDIX B: BIBLIOGRAPHY . 231
APPENDIX C: ACKNOWLEDGEMENTS .


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