L'édition de cet ISBN n'est malheureusement plus disponible.
Afficher les exemplaires de cette édition ISBN
Frais de port :
EUR 2,43
Vers Etats-Unis
Description du livre Etat : New. N° de réf. du vendeur 46661374-n
Description du livre Soft Cover. Etat : new. This item is printed on demand. N° de réf. du vendeur 9780262549943
Description du livre Etat : New. N° de réf. du vendeur I-9780262549943
Description du livre PAP. Etat : New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. N° de réf. du vendeur L0-9780262549943
Description du livre Etat : New. In. N° de réf. du vendeur ria9780262549943_new
Description du livre Etat : New. Buy with confidence! Book is in new, never-used condition 0.81. N° de réf. du vendeur bk0262549948xvz189zvxnew
Description du livre Paperback / softback. Etat : New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days. N° de réf. du vendeur C9780262549943
Description du livre Paperback. Etat : new. Paperback. In nearly all machine learning, decisions must be made given current knowledge. Surprisingly, making what is believed to be the best decision is not always the best strategy, even when learning in a supervised learning setting. An emerging body of work on learning under different rules applies perturbations to decision and learning procedures. These methods provide simple and highly efficient learning rules with improved theoretical guarantees. This book describes perturbation-based methods developed in machine learning to augment novel optimization methods with strong statistical guarantees, offering readers a state-of-the-art overview.Chapters address recent modeling ideas that have arisen within the perturbations framework, including Perturb & MAP, herding, and the use of neural networks to map generic noise to distribution over highly structured data. They describe new learning procedures for perturbation models, including an improved EM algorithm and a learning algorithm that aims to match moments of model samples to moments of data. They discuss understanding the relation of perturbation models to their traditional counterparts, with one chapter showing that the perturbations viewpoint can lead to new algorithms in the traditional setting. And they consider perturbation-based regularization in neural networks, offering a more complete understanding of dropout and studying perturbations in the context of deep neural networks.A description of perturbation-based methods developed in machine learning to augment novel optimization methods with strong statistical guarantees.In nearly all machine learning, decisions must be made given current knowledge. Surprisingly, making what is believed to be the best decision is not always the best strategy, even when learning in a supervised learning setting. An emerging body of work on learning under different rules applies perturbations to decision and learning procedures. These methods provide simple and highly efficient learning rules with improved theoretical guarantees. This book describes perturbation-based methods developed in machine learning to augment novel optimization methods with strong statistical guarantees, offering readers a state-of-the-art overview.Chapters address recent modeling ideas that have arisen within the perturbations framework, including Perturb & MAP, herding, and the use of neural networks to map generic noise to distribution over highly structured data. They describe new learning procedures for perturbation models, including an improved EM algorithm and a learning algorithm that aims to match moments of model samples to moments of data. They discuss understanding the relation of perturbation models to their traditional counterparts, with one chapter showing that the perturbations viewpoint can lead to new algorithms in the traditional setting. And they consider perturbation-based regularization in neural networks, offering a more complete understanding of dropout and studying perturbations in the context of deep neural networks. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9780262549943
Description du livre Etat : New. N° de réf. du vendeur 46661374-n
Description du livre PAP. Etat : New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. N° de réf. du vendeur L0-9780262549943