Meta-Algorithmics: Patterns for Robust, Low Cost, High Quality Systems (英语) 精装 – 2013年7月29日

Steven J. Simske (作者)

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The confluence of cloud computing, parallelism and advancedmachine intelligence approaches has created a world in which theoptimum knowledge system will usually be architected from thecombination of two or more knowledge-generating systems. There is aneed, then, to provide a reusable, broadly-applicable set of designpatterns to empower the intelligent system architect to takeadvantage of this opportunity.

This book explains how to design and build intelligent systemsthat are optimized for changing system requirements (adaptability),optimized for changing system input (robustness), and optimized forone or more other important system parameters (e.g.,accuracy, efficiency, cost). It provides an overview of traditionalparallel processing which is shown to consist primarily of task andcomponent parallelism; before introducing meta-algorithmicparallelism which is based on combining two or more algorithms,classification engines or other systems.

Key features:

Explains the entire roadmap for the design, testing,development, refinement, deployment and statistics-drivenoptimization of building systems for intelligence
Offers an accessible yet thorough overview of machineintelligence, in addition to having a strong image processingfocus
Contains design patterns for parallelism, especiallymeta-algorithmic parallelism – simply conveyed, reusable andproven effective that can be readily included in the toolbox ofexperts in analytics, system architecture, big data, security andmany other science and engineering disciplines
Connects algorithms and analytics to parallelism, therebyillustrating a new way of designing intelligent systems compatiblewith the tremendous changes in the computing world over the pastdecade
Discusses application of the approaches to a wide number offields; primarily, document understanding, image understanding,biometrics and security printing
Companion website contains sample code and data sets

基本信息

出版社: Wiley-IEEE Press; 1 (2013年7月29日)
丛书名: Wiley - IEEE
精装: 386页
语种： 英语
ISBN: 1118343360
条形码: 9781118343364
商品尺寸: 18 x 2.4 x 25.3 cm
商品重量: 739 g
ASIN: 1118343360
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作者简介

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 inalgorithms, imaging, machine learning and classification for thepast 20 years. As an engineer at HP Labs, he has designed,developed and shipped products associated with a very broad arrayof domains—document understanding, image segmentation andunderstanding, speech recognition, medical signal processing andimaging, biometrics, natural language processing, surveillance,optical character recognition, security analytics and securityprinting. The advantages of systematic meta-algorithmic approachesto the robustness, accuracy, cost and/or other system featureswhich is the focus of the book has been evident across thesedomains. Dr. Simske is an HP Fellow, IS&T Fellow and IEEESenior Member. He has published 300 articles and book chapters; andholds 45 US Patents primarily in the areas of classification,machine learning, and large system design and development.

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

1.7 Artificial Intelligence 22

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

2.3 Parallelism by Component 52

2.4 Parallelism by Meta-algorithm 64

2.5 Summary 71

References 72

3 Domain Areas: Where Is This Relevant? 73

3.1 Introduction 73

3.2 Overview of the Domains 74

3.3 Primary Domains 75

3.4 Secondary Domains 86

3.5 Summary 101

References 102

4 Applications of Parallelism by Task 104

4.1 Introduction 104

4.2 Primary Domains 105

4.3 Summary 135

References 136

5 Application of Parallelism by Component 137

5.1 Introduction 137

5.2 Primary Domains 138

5.3 Summary 172

References 173

6 Introduction to Meta-algorithmics 175

6.1 Introduction 175

6.2 First-Order Meta-algorithmics 178

6.3 Second-Order Meta-algorithmics 195

6.4 Third-Order Meta-algorithmics 218

6.5 Summary 240

References 240

7 First-Order Meta-algorithmics and Their Applications 241

7.1 Introduction 241

7.2 First-Order Meta-algorithmics and the “Black Box”241

7.3 Primary Domains 242

7.4 Secondary Domains 257

7.5 Summary 271

References 271

8 Second-Order Meta-algorithmics and Their Applications272

8.1 Introduction 272

8.2 Second-Order Meta-algorithmics and Targeting the“Fringes” 273

8.3 Primary Domains 279

8.4 Secondary Domains 304

8.5 Summary 308

References 308

9 Third-Order Meta-algorithmics and Their Applications 310

9.1 Introduction 310

9.2 Third-Order Meta-algorithmic Patterns 311

9.3 Primary Domains 313

9.4 Secondary Domains 328

9.5 Summary 340

References 341

10 Building More Robust Systems 342

10.1 Introduction 342

10.2 Summarization 342

10.3 Cloud Systems 350

10.4 Mobile Systems 353

10.5 Scheduling 355

10.6 Classification 356

10.7 Summary 358

Reference 359

11 The Future 360

11.1 Recapitulation 360

11.2 The Pattern of all Patience 362

11.3 Beyond the Pale 365

11.4 Coming Soon 367

11.5 Summary 368

References 368

Index

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Superb layered patterns for the system architect

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I build intelligent decision support systems for a living, and I found this to be an excellent primer on how to apply a range of patterns to improve the quality, costs, and robustness of these kinds of systems. With the evolution of elastic resources, it is now possible to provide a number of more targeted algorithms rather than a one size fits all approach. The craft is in how to combine these targeted approaches to get a better result, and the text is excellent at introducing the patterns and providing real examples of recognition, biometrics, and security systems built on these patterns. The author introduces three levels of patterns that build on the previous level. The first order patterns provide general approaches to combining multiple sources of knowledge, and include one pattern that demonstrates how combining algorithms can produce emergent results not seen by the individual algorithms. The second order patterns are the heart of the discussion, as they provide specific approaches to combining the outputs in creative ways with weighted analysis and intelligent transformations. The third order patterns are about the feedback loop to learn over time.

I enjoyed this book because it provided easy to understand patterns that provided the foundation for more complex approaches, and showed how the patterns built on each other. It provided examples that were easy to understand in image or voice recognition, biometrics, and security which made it concrete rather than many abstract examples. The author is well-versed in the application of these patterns, and provides insightful perspectives on when and how to apply the patterns.

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