Mathematical Theories of Machine Learning - Theory and Applications
-
- Hardcover
- Taschenbuch ausgewählt
- eBook
-
Sprache:Englisch
Fr. 122.00
inkl. gesetzl. MwSt.,
Beschreibung
Produktdetails
Einband
Taschenbuch
Erscheinungsdatum
14.08.2020
Verlag
SpringerSeitenzahl
133
Maße (L/B/H)
23.5/15.5/0.9 cm
Gewicht
248 g
Auflage
1st ed. 2020
Sprache
Englisch
ISBN
978-3-030-17078-3
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.
Kundinnen und Kunden meinen
Verfassen Sie die erste Bewertung zu diesem Artikel
Helfen Sie anderen Kund*innen durch Ihre Meinung