Mathematical Theories of Machine Learning - Theory and Applications (Hardcover, 1st ed. 2020)

This book studies mathematical theories of machine learning. The first part of the book explores the optimality and adaptivity of choosing step sizes of gradient descent for escaping strict saddle points in non-convex optimization problems. In the second part, the authors propose algorithms to find local minima in nonconvex optimization and to obtain global minima in some degree from the Newton Second Law without friction. In the third part, the authors study the problem of subspace clustering with noisy and missing data, which is a problem well-motivated by practical applications data subject to stochastic Gaussian noise and/or incomplete data with uniformly missing entries. In the last part, the authors introduce an novel VAR model with Elastic-Net regularization and its equivalent Bayesian model allowing for both a stable sparsity and a group selection.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Release date:	June 2019
First published:	2020
Authors:	Bin Shi • S.S. Iyengar
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	133
Edition:	1st ed. 2020
ISBN-13:	978-3-03-017075-2
Categories:	Books > Business & Economics > Business & management > Business mathematics & systems > General Books > Professional & Technical > Electronics & communications engineering > Communications engineering / telecommunications > General Books > Computing & IT > Applications of computing > Databases > Data mining Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
LSN:	3-03-017075-6
Barcode:	9783030170752

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