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Meta-Algorithmics : Patterns for Robust, Low Cost, High Quality Systems.
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-ap...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken :
Wiley,
2013.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Simske, Steven J. | |
| 245 | 1 | 0 | |a Meta-Algorithmics : |b Patterns for Robust, Low Cost, High Quality Systems. |
| 260 | |a Hoboken : |b Wiley, |c 2013. | ||
| 300 | |a 1 online resource (388 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a META-ALGORITHMICS; Contents; Acknowledgments; 1 Introduction and Overview; 1.1 Introduction; 1.2 Why Is This Book Important?; 1.3 Organization of the Book; 1.4 Informatics; 1.5 Ensemble Learning; 1.6 Machine Learning/Intelligence; 1.6.1 Regression and Entropy; 1.6.2 SVMs and Kernels; 1.6.3 Probability; 1.6.4 Unsupervised Learning; 1.6.5 Dimensionality Reduction; 1.6.6 Optimization and Search; 1.7 Artificial Intelligence; 1.7.1 Neural Networks; 1.7.2 Genetic Algorithms; 1.7.3 Markov Models; 1.8 Data Mining/Knowledge Discovery; 1.9 Classification; 1.10 Recognition; 1.11 System-Based Analysis. | |
| 505 | 8 | |a 1.12 SummaryReferences; 2 Parallel Forms of Parallelism; 2.1 Introduction; 2.2 Parallelism by Task; 2.2.1 Definition; 2.2.2 Application to Algorithms and Architectures; 2.2.3 Application to Scheduling; 2.3 Parallelism by Component; 2.3.1 Definition and Extension to Parallel-Conditional Processing; 2.3.2 Application to Data Mining, Search, and Other Algorithms; 2.3.3 Application to Software Development; 2.4 Parallelism by Meta-algorithm; 2.4.1 Meta-algorithmics and Algorithms; 2.4.2 Meta-algorithmics and Systems; 2.4.3 Meta-algorithmics and Parallel Processing. | |
| 505 | 8 | |a 2.4.4 Meta-algorithmics and Data Collection2.4.5 Meta-algorithmics and Software Development; 2.5 Summary; References; 3 Domain Areas: Where Are These Relevant?; 3.1 Introduction; 3.2 Overview of the Domains; 3.3 Primary Domains; 3.3.1 Document Understanding; 3.3.2 Image Understanding; 3.3.3 Biometrics; 3.3.4 Security Printing; 3.4 Secondary Domains; 3.4.1 Image Segmentation; 3.4.2 Speech Recognition; 3.4.3 Medical Signal Processing; 3.4.4 Medical Imaging; 3.4.5 Natural Language Processing; 3.4.6 Surveillance; 3.4.7 Optical Character Recognition; 3.4.8 Security Analytics; 3.5 Summary. | |
| 504 | |a References4 Applications of Parallelism by Task; 4.1 Introduction; 4.2 Primary Domains; 4.2.1 Document Understanding; 4.2.2 Image Understanding; 4.2.3 Biometrics; 4.2.4 Security Printing; 4.3 Summary; References; 5 Application of Parallelism by Component; 5.1 Introduction; 5.2 Primary Domains; 5.2.1 Document Understanding; 5.2.2 Image Understanding; 5.2.3 Biometrics; 5.2.4 Security Printing; 5.3 Summary; References; 6 Introduction to Meta-algorithmics; 6.1 Introduction; 6.2 First-Order Meta-algorithmics; 6.2.1 Sequential Try; 6.2.2 Constrained Substitute; 6.2.3 Voting and Weighted Voting. | ||
| 505 | 8 | |a 6.2.4 Predictive Selection6.2.5 Tessellation and Recombination; 6.3 Second-Order Meta-algorithmics; 6.3.1 Confusion Matrix and Weighted Confusion Matrix; 6.3.2 Confusion Matrix with Output Space Transformation (Probability Space Transformation); 6.3.3 Tessellation and Recombination with Expert Decisioner; 6.3.4 Predictive Selection with Secondary Engines; 6.3.5 Single Engine with Required Precision; 6.3.6 Majority Voting or Weighted Confusion Matrix; 6.3.7 Majority Voting or Best Engine; 6.3.8 Best Engine with Differential Confidence or Second Best Engine. | |
| 500 | |a 6.3.9 Best Engine with Absolute Confidence or Weighted Confusion Matrix. | ||
| 520 | |a 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 (robustne. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Computer algorithms. | |
| 650 | 0 | |a Computer systems |x Costs. | |
| 650 | 0 | |a Computer systems |x Quality control. | |
| 650 | 0 | |a Heuristic programming. | |
| 650 | 0 | |a Parallel algorithms. | |
| 650 | 0 | |a Algorithms. | |
| 650 | 6 | |a Algorithmes. | |
| 650 | 6 | |a Systèmes informatiques |x Coût. | |
| 650 | 6 | |a Systèmes informatiques |x Qualité |x Contrôle. | |
| 650 | 6 | |a Programmation heuristique. | |
| 650 | 6 | |a Algorithmes parallèles. | |
| 650 | 7 | |a algorithms. |2 aat | |
| 650 | 7 | |a Algorithms |2 fast | |
| 650 | 7 | |a Computer algorithms |2 fast | |
| 650 | 7 | |a Heuristic programming |2 fast | |
| 650 | 7 | |a Parallel algorithms |2 fast | |
| 758 | |i has work: |a Meta-algorithmics (Text) |1 https://id.oclc.org/worldcat/entity/E39PCH6Kvvp8WFyrWMgH943kH3 |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Simske, Steven J. |t Meta-Algorithmics : Patterns for Robust, Low Cost, High Quality Systems. |d Hoboken : Wiley, ©2013 |z 9781118343364 |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=1207855 |z Texto completo |
| 938 | |a EBL - Ebook Library |b EBLB |n EBL1207855 | ||
| 994 | |a 92 |b IZTAP | ||