Seller: Bellwetherbooks, McKeesport, PA, U.S.A.
hardcover. Condition: Very Good. Very Good Condition - May show some limited signs of wear and may have a remainder mark. Pages and dust cover are intact and not marred by notes or highlighting.
Seller: Bellwetherbooks, McKeesport, PA, U.S.A.
hardcover. Condition: New.
Condition: As New. Unread book in perfect condition.
Condition: New.
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Condition: New.
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Condition: As New. Unread book in perfect condition.
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
Paperback. Condition: New.
Seller: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Ireland
Condition: New.
Paperback. Condition: Brand New. 412 pages. 10.00x8.00x1.03 inches. In Stock.
Seller: Kennys Bookstore, Olney, MD, U.S.A.
Condition: New.
Paperback. Condition: New.
Seller: Mispah books, Redhill, SURRE, United Kingdom
paperback. Condition: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
£ 68.45
Quantity: Over 20 available
Add to basketPAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Seller: PBShop.store US, Wood Dale, IL, U.S.A.
PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Seller: Biblios, Frankfurt am main, HESSE, Germany
Condition: New. PRINT ON DEMAND.
Seller: Majestic Books, Hounslow, United Kingdom
Condition: New. Print on Demand.
Seller: Books Puddle, New York, NY, U.S.A.
Condition: New. Print on Demand.
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
£ 79.27
Quantity: Over 20 available
Add to basketPaperback / softback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.
Seller: moluna, Greven, Germany
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Tamir Hazan is Assistant Professor at Technion, Israel Institute of Technology.George Papandreou is a Research Scientist for Google, Inc.Daniel Tarlow is a Researcher at Microsoft Research Cambridge, UK.A description of perturbation-b.
Seller: preigu, Osnabrück, Germany
Taschenbuch. Condition: Neu. Perturbations, Optimization, and Statistics | Tamir Hazan (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2023 | MIT Press | EAN 9780262549943 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
Seller: AHA-BUCH GmbH, Einbeck, Germany
Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.