Meta-Algorithmics
Patterns for Robust, Low Cost, High Quality Systems
Published on 28. May 2013
392 pages
978-1-118-62670-2 (ISBN)
Description
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
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Book
07/2013
Wiley-IEEE Press
€106.50
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05/2013
Wiley
€115.85
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Person
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.
Content
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
