Mathematical Theories of Machine Learning - Theory and Applications
Mathematical Theories of Machine Learning - Theory and Applications by Bin Shi
English | EPUB | 2020 | 138 Pages | ISBN : 3030170756 | 12.02 MB
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.
i will be very grateful when you support me and buy Or Renew Your Premium from my Blog links
i appreciate your support Too much as it will help me to post more and more
Without You And Your Support We Can’t Continue
Thanks For Buying Premium From My Links For Support
i appreciate your support Too much as it will help me to post more and more
Without You And Your Support We Can’t Continue
Thanks For Buying Premium From My Links For Support