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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 |
Tabla de Contenidos:
- 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.
- 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.
- 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.
- 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.