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Swarm Intelligence Methods for Statistical Regression / by Soumya Mohanty.

By: Mohanty, Soumya [author.].
Contributor(s): Taylor and Francis.
Material type: materialTypeLabelBookPublisher: Boca Raton, FL : Chapman and Hall/CRC, [2018]Copyright date: ©2019Edition: First edition.Description: 1 online resource (136 pages) : 19 illustrations, text file, PDF.Content type: text Media type: computer Carrier type: online resourceISBN: 9781315151274(e-book : PDF).Subject(s): COMPUTERS / Database Management / Data Mining | COMPUTERS / Machine Theory | data analysis | genetic algorithms | high-dimensional data | multi-agent systems | optimization | parametic regression | Big data | Swarm intelligence | Computational intelligence | Regression analysisGenre/Form: Electronic books.Additional physical formats: Print version: : No titleDDC classification: 5.7 Online resources: Click here to view Also available in print format.
Contents:
Chapter 1 Introduction Chapter 2 Stochastic Optimization Theory Chapter 3 Evolutionary Computation and Swarm Intelligence Chapter 4 Particle Swarm Optimization Chapter 5 PSO Applications Appendix A Probability Theory Appendix B Splines Appendix C Analytical minimization.
Abstract: A core task in statistical analysis, especially in the era of Big Data, is the fitting of flexible, high-dimensional, and non-linear models to noisy data in order to capture meaningful patterns. This can often result in challenging non-linear and non-convex global optimization problems. The large data volume that must be handled in Big Data applications further increases the difficulty of these problems. Swarm Intelligence Methods for Statistical Regression describes methods from the field of computational swarm intelligence (SI), and how they can be used to overcome the optimization bottleneck encountered in statistical analysis. Features Provides a short, self-contained overview of statistical data analysis and key results in stochastic optimization theory Focuses on methodology and results rather than formal proofs Reviews SI methods with a deeper focus on Particle Swarm Optimization (PSO) Uses concrete and realistic data analysis examples to guide the reader Includes practical tips and tricks for tuning PSO to extract good performance in real world data analysis challenges.
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Includes bibliographical references and index.

Chapter 1 Introduction Chapter 2 Stochastic Optimization Theory Chapter 3 Evolutionary Computation and Swarm Intelligence Chapter 4 Particle Swarm Optimization Chapter 5 PSO Applications Appendix A Probability Theory Appendix B Splines Appendix C Analytical minimization.

A core task in statistical analysis, especially in the era of Big Data, is the fitting of flexible, high-dimensional, and non-linear models to noisy data in order to capture meaningful patterns. This can often result in challenging non-linear and non-convex global optimization problems. The large data volume that must be handled in Big Data applications further increases the difficulty of these problems. Swarm Intelligence Methods for Statistical Regression describes methods from the field of computational swarm intelligence (SI), and how they can be used to overcome the optimization bottleneck encountered in statistical analysis. Features Provides a short, self-contained overview of statistical data analysis and key results in stochastic optimization theory Focuses on methodology and results rather than formal proofs Reviews SI methods with a deeper focus on Particle Swarm Optimization (PSO) Uses concrete and realistic data analysis examples to guide the reader Includes practical tips and tricks for tuning PSO to extract good performance in real world data analysis challenges.

Also available in print format.

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