Isbn: 9783030170752 - mathematical theories of machine learning - theory and applications (16 resultados)

Condición: As New. Unread book in perfect condition.

Condición: New. In English.

Condición: New.

Condición: New.

Condición: As New. Unread book in perfect condition.

Condición: New.

Condición: New.

Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.…

Hardcover. Condición: Brand New. 156 pages. 9.25x6.10x0.63 inches. In Stock.

Condición: New.

Condición: new. Questo è un articolo print on demand.

Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 156 pp. Englisch.…

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Provides a thorough look into the variety of mathematical theories of machine learningPresented in four parts, allowing for readers to easily navigate the complex theories&nbspIncludes extensive empirical studies on both the synthetic and .…

Condición: New. Print on Demand.

Condición: New. PRINT ON DEMAND.

Buch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 156 pp. Englisch.…