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Bandit Algorithms
Published by Cambridge University Press (edition 1), 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Language: English
Hardcover. Condition: Good. 1. It's a preowned item in good condition and includes all the pages. It may have some general signs of wear and tear, such as markings, highlighting, slight damage to the cover, minimal wear to the binding, etc., but they will not affect the overall reading experience.
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Bandit Algorithms
Published by Cambridge University Press, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Language: English
Condition: good. Signs of wear and consistent use.
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Bandit Algorithms
Published by Cambridge University Press, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Language: English
Seller: Books From California, Simi Valley, CA, U.S.A.
Seller rating 5 out of 5 stars
hardcover. Condition: Very Good. Cover and edges may have some wear.
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Bandit Algorithms
Published by Cambridge University Press, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Language: English
Condition: New.
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Bandit Algorithms
Published by Cambridge University Press, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Language: English
Condition: New.
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Bandit Algorithms (Hardback or Cased Book)
Published by Cambridge University Press 9/10/2020, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Language: English
Hardback or Cased Book. Condition: New. Bandit Algorithms. Book.
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Bandit Algorithms
Published by Cambridge University Press, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Language: English
Condition: As New. Unread book in perfect condition.
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Bandit Algorithms (Hardcover)
Published by Cambridge University Press, Cambridge, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Language: English
Hardcover. Condition: new. Hardcover. Decision-making in the face of uncertainty is a significant challenge in machine learning, and the multi-armed bandit model is a commonly used framework to address it. This comprehensive and rigorous introduction to the multi-armed bandit problem examines all the major settings, including stochastic, adversarial, and Bayesian frameworks. A focus on both mathematical intuition and carefully worked proofs makes this an excellent reference for established researchers and a helpful resource for graduate students in computer science, engineering, statistics, applied mathematics and economics. Linear bandits receive special attention as one of the most useful models in applications, while other chapters are dedicated to combinatorial bandits, ranking, non-stationary problems, Thompson sampling and pure exploration. The book ends with a peek into the world beyond bandits with an introduction to partial monitoring and learning in Markov decision processes. Decision-making in the face of uncertainty is a challenge in machine learning, and the multi-armed bandit model is a common framework to address it. This comprehensive introduction is an excellent reference for established researchers and a resource for graduate students interested in exploring stochastic, adversarial and Bayesian frameworks. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Bandit Algorithms
Published by Cambridge University Press, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Language: English
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
Condition: New. In.
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Bandit Algorithms
Published by Cambridge University Press 2020-07-16, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Language: English
Hardcover. Condition: New.
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Bandit Algorithms
Published by Cambridge University Press, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Language: English
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: New.
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Bandit Algorithms
Published by Cambridge University Press, GB, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Language: English
Hardback. Condition: New. Decision-making in the face of uncertainty is a significant challenge in machine learning, and the multi-armed bandit model is a commonly used framework to address it. This comprehensive and rigorous introduction to the multi-armed bandit problem examines all the major settings, including stochastic, adversarial, and Bayesian frameworks. A focus on both mathematical intuition and carefully worked proofs makes this an excellent reference for established researchers and a helpful resource for graduate students in computer science, engineering, statistics, applied mathematics and economics. Linear bandits receive special attention as one of the most useful models in applications, while other chapters are dedicated to combinatorial bandits, ranking, non-stationary problems, Thompson sampling and pure exploration. The book ends with a peek into the world beyond bandits with an introduction to partial monitoring and learning in Markov decision processes.
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Bandit Algorithms
Published by Cambridge University Press, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Language: English
Seller: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Ireland
Seller rating 5 out of 5 stars
Condition: New. 2020. Hardcover. . . . . .
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Bandit Algorithms
Published by Cambridge University Press, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Language: English
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: As New. Unread book in perfect condition.
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Bandit Algorithms
Published by Cambridge University Press CUP, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Language: English
Condition: New.
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Bandit Algorithms
Published by Cambridge University Press, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Language: English
Condition: New. 2020. Hardcover. . . . . . Books ship from the US and Ireland.
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Bandit Algorithms
Published by Cambridge University Press, GB, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Language: English
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
Seller rating 5 out of 5 stars
Hardback. Condition: New. Decision-making in the face of uncertainty is a significant challenge in machine learning, and the multi-armed bandit model is a commonly used framework to address it. This comprehensive and rigorous introduction to the multi-armed bandit problem examines all the major settings, including stochastic, adversarial, and Bayesian frameworks. A focus on both mathematical intuition and carefully worked proofs makes this an excellent reference for established researchers and a helpful resource for graduate students in computer science, engineering, statistics, applied mathematics and economics. Linear bandits receive special attention as one of the most useful models in applications, while other chapters are dedicated to combinatorial bandits, ranking, non-stationary problems, Thompson sampling and pure exploration. The book ends with a peek into the world beyond bandits with an introduction to partial monitoring and learning in Markov decision processes.
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BANDIT ALGORITHMS
Published by Cambridge University Press, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Language: English
Condition: NEW.
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Hardcover. Condition: Brand New. 517 pages. 9.50x7.00x1.25 inches. In Stock.
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Condition: New. Decision-making in the face of uncertainty is a challenge in machine learning, and the multi-armed bandit model is a common framework to address it. This comprehensive introduction is an excellent reference for established researchers and a resource for gra.
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Bandit Algorithms
Published by Cambridge University Press, GB, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Language: English
Hardback. Condition: New. Decision-making in the face of uncertainty is a significant challenge in machine learning, and the multi-armed bandit model is a commonly used framework to address it. This comprehensive and rigorous introduction to the multi-armed bandit problem examines all the major settings, including stochastic, adversarial, and Bayesian frameworks. A focus on both mathematical intuition and carefully worked proofs makes this an excellent reference for established researchers and a helpful resource for graduate students in computer science, engineering, statistics, applied mathematics and economics. Linear bandits receive special attention as one of the most useful models in applications, while other chapters are dedicated to combinatorial bandits, ranking, non-stationary problems, Thompson sampling and pure exploration. The book ends with a peek into the world beyond bandits with an introduction to partial monitoring and learning in Markov decision processes.
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Bandit Algorithms
Published by Cambridge University Press, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Language: English
hardcover. Condition: Neu. Neu Neuware, Importqualität, auf Lager.
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Bandit Algorithms
Published by Cambridge University Press, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Language: English
Buch. Condition: Neu. Bandit Algorithms | Tor Lattimore (u. a.) | Buch | Gebunden | Englisch | 2020 | Cambridge University Press | EAN 9781108486828 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.
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Bandit Algorithms
Published by Cambridge University Press, GB, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Language: English
Hardback. Condition: New. Decision-making in the face of uncertainty is a significant challenge in machine learning, and the multi-armed bandit model is a commonly used framework to address it. This comprehensive and rigorous introduction to the multi-armed bandit problem examines all the major settings, including stochastic, adversarial, and Bayesian frameworks. A focus on both mathematical intuition and carefully worked proofs makes this an excellent reference for established researchers and a helpful resource for graduate students in computer science, engineering, statistics, applied mathematics and economics. Linear bandits receive special attention as one of the most useful models in applications, while other chapters are dedicated to combinatorial bandits, ranking, non-stationary problems, Thompson sampling and pure exploration. The book ends with a peek into the world beyond bandits with an introduction to partial monitoring and learning in Markov decision processes.
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Hardcover. Condition: Brand New. 517 pages. 9.50x7.00x1.25 inches. In Stock. This item is printed on demand.
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Bandit Algorithms
Published by Cambridge University Press, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Language: English
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
Seller rating 5 out of 5 stars
Hardback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 1220.
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Bandit Algorithms
Published by Cambridge University Press, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Language: English
Condition: New. Print on Demand.
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Bandit Algorithms
Published by Cambridge University Press, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Language: English
Condition: New. PRINT ON DEMAND.
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Bandit Algorithms (Hardcover)
Published by Cambridge University Press, Cambridge, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Language: English
Hardcover. Condition: new. Hardcover. Decision-making in the face of uncertainty is a significant challenge in machine learning, and the multi-armed bandit model is a commonly used framework to address it. This comprehensive and rigorous introduction to the multi-armed bandit problem examines all the major settings, including stochastic, adversarial, and Bayesian frameworks. A focus on both mathematical intuition and carefully worked proofs makes this an excellent reference for established researchers and a helpful resource for graduate students in computer science, engineering, statistics, applied mathematics and economics. Linear bandits receive special attention as one of the most useful models in applications, while other chapters are dedicated to combinatorial bandits, ranking, non-stationary problems, Thompson sampling and pure exploration. The book ends with a peek into the world beyond bandits with an introduction to partial monitoring and learning in Markov decision processes. Decision-making in the face of uncertainty is a challenge in machine learning, and the multi-armed bandit model is a common framework to address it. This comprehensive introduction is an excellent reference for established researchers and a resource for graduate students interested in exploring stochastic, adversarial and Bayesian frameworks. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
-
Bandit Algorithms (Hardcover)
Published by Cambridge University Press, Cambridge, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Language: English
Hardcover. Condition: new. Hardcover. Decision-making in the face of uncertainty is a significant challenge in machine learning, and the multi-armed bandit model is a commonly used framework to address it. This comprehensive and rigorous introduction to the multi-armed bandit problem examines all the major settings, including stochastic, adversarial, and Bayesian frameworks. A focus on both mathematical intuition and carefully worked proofs makes this an excellent reference for established researchers and a helpful resource for graduate students in computer science, engineering, statistics, applied mathematics and economics. Linear bandits receive special attention as one of the most useful models in applications, while other chapters are dedicated to combinatorial bandits, ranking, non-stationary problems, Thompson sampling and pure exploration. The book ends with a peek into the world beyond bandits with an introduction to partial monitoring and learning in Markov decision processes. Decision-making in the face of uncertainty is a challenge in machine learning, and the multi-armed bandit model is a common framework to address it. This comprehensive introduction is an excellent reference for established researchers and a resource for graduate students interested in exploring stochastic, adversarial and Bayesian frameworks. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
