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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.
List of 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
About the author
Soumya D. Mohanty, Professor of Physics at the University of Texas Rio Grande Valley, completed his PhD degree in 1997 at the Inter-University Center for Astronomy and Astrophysics, India. He subsequently held post-doctoral positions at Northwestern University, Penn State, and the Max-Planck Institute for Gravitational Physics. He was also a visiting scholar with the LIGO project at Caltech. Mohanty's research has focused on solving some of the important data analysis challenges faced in Gravitational Wave (GW) astronomy across all observational frequency bands. These include non-parametric regression of very weak signals in noisy data, high-dimensional non-linear parametric regression, time series classification, and analysis of data from large heterogeneous sensor arrays. Mohanty’s work has been funded by grants from the Research Corporation, the U.S. National Science Foundation, and NASA.
Summary
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.
