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...
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...
This book provides a very readable introduction to swarm intelligence methods, which are useful for solving optimization problems that arise in many fields of science and engineering. By focusing on one particular method (Particle Swarm Optimization) applied to two specific problems, Mohanty gives readers a solid foundation for further investigation into other swarm intelligence methods. A must read for any researcher who wants to stay abreast of this relatively...
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