Meta-Algorithmics - Steven J Simske

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Résumé :

The confluence of cloud computing, parallelism and advanced machine intelligence approaches has created a world in which the optimum knowledge system will usually be architected from the combination of two or more knowledge-generating systems. There is a need, then, to provide a reusable, broadly-applicable set of design patterns to empower the intelligent system architect to take advantage of this opportunity.

This book explains how to design and build intelligent systems that are optimized for changing system requirements (adaptability), optimized for changing system input (robustness), and optimized for one or more other important system? parameters (e.g., accuracy, efficiency, cost). It provides an overview of traditional parallel processing which is shown to consist primarily of task and component parallelism...

Biographie:

Steven J. Simske, Hewlett-Packard Labs, Colorado, USA
Dr Simske is currently Director of the Document Ecosystem Lab, at Hewlett-Packard Labs, Colorado, USA. He has been working in algorithms, imaging, machine learning and classification for the past 20 years. As an engineer at HP Labs, he has designed, developed and shipped products associated with a very broad array of domains-document understanding, image segmentation and understanding, speech recognition, medical signal processing and imaging, biometrics, natural language processing, surveillance, optical character recognition, security analytics and security printing. The advantages of systematic meta-algorithmic approaches to the robustness, accuracy, cost and/or other system features which is the focus of the book has been evident across these domains. Dr. Simske is an HP Fellow, IS&T Fellow and IEEE Senior Member. He has published 300 articles and book chapters...

Sommaire:

Acknowledgments xi

1 Introduction and Overview 1

1.1 Introduction 1

1.2 Why Is This Book Important? 2

1.3 Organization of the Book 3

1.4 Informatics 4

1.5 Ensemble Learning 6

1.6 Machine Learning/Intelligence 7

1.6.1 Regression and Entropy 8

1.6.2 SVMs and Kernels 9

1.6.3 Probability 15

1.6.4 Unsupervised Learning 17

1.6.5 Dimensionality Reduction 18

1.6.6 Optimization and Search 20

1.7 Artificial Intelligence 22

1.7.1 Neural Networks 22

1.7.2 Genetic Algorithms 25

1.7.3 Markov Models 28

1.8 Data Mining/Knowledge Discovery 31

1.9 Classification 32

1.10 Recognition 38

1.11 System-Based Analysis 39

1.12 Summary 39

References 40

2 Parallel Forms of Parallelism 42

2.1 Introduction 42

2.2 Parallelism by Task 43

2.2.1 Definition 43

2.2.2 Application to Algorithms and Architectures 46

2.2.3 Application to Scheduling 51

2.3 Parallelism by Component 52

2.3.1 Definition and Extension to Parallel-Conditional Processing 52

2.3.2 Application to Data Mining, Search, and Other Algorithms 55

2.3.3 Application to Software Development 59

2.4 Parallelism by Meta-algorithm 64

2.4.1 Meta-algorithmics and Algorithms 66

2.4.2 Meta-algorithmics and Systems 67

2.4.3 Meta-algorithmics and Parallel Processing 68

2.4.4 Meta-algorithmics and Data Collection 69

2.4.5 Meta-algorithmics and Software Development 70

2.5 Summary 71

References 72

3 Domain Areas: Where Are These Relevant? 73

3.1 Introduction 73

3.2 Overview of the Domains 74

3.3 Primary Domains 75

3.3.1 Document Understanding 75

3.3.2 Image Understanding 77

3.3.3 Biometrics 78

3.3.4 Security Printing 79

3.4 Secondary Domains 86

3.4.1 Image Segmentation 86

3.4.2 Speech Recognition 90

3.4.3 Medical Signal Processing 90

3.4.4 Medical Imaging 92

3.4.5 Natural Language Processing 95

3.4.6 Surveillance 97

3.4.7 Optical Character Recognition 98

3.4.8 Security Analytics 101

3.5 Summary 101

References 102

4 Applications of Parallelism by Task 104

4.1 Introduction 104

4.2 Primary Domains 105

4.2.1 Document Understanding 112

4.2.2 Image Understanding 118

4.2.3 Biometrics 126

4.2.4 Security Printing 131

4.3 Summary 135

References 136

5 Application of Parallelism by Component 137

5.1 Introduction 137

5.2 Primary Domains 138

5.2.1 Document Understanding 138

5.2.2 Image Understanding 152

5.2.3 Biometrics 162

5.2.4 Security Printing 170

5.3 Summary 172

References 173

6 Introduction to Meta-algorithmics 175

6.1 Introduction 175

6.2 First-Order Meta-algorithmics 178

6.2.1 Sequential Try 178

6.2.2 Constrained Substitute 181

6.2.3 Voting and Weighted Voting 184

6.2.4 Predictive Selection 189

6.2.5 Tessellation and Recombination 192

6.3 Second-Order Meta-algorithmics 195

6.3.1 Confusion Matrix and Weighted Confusion Matrix 195

6.3.2 Confusion Matrix with Output Space Transformation (Probability Space Transformation) 199

6.3.3 Tessellation and Recombination with Expert Decisioner 203

6.3.4 Predictive Selection with Secondary Engines 206

6.3.5 Single Engine with Required Precision 208

6.3.6 Majority Voting or Weighted Confusion Matrix 209

6.3.7 Majority Voting or Best Engine 210

6.3.8 Best Engine with Differential Confidence o...