Editore: Cambridge University Press, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Lingua: Inglese
Da: Books From California, Simi Valley, CA, U.S.A.
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Editore: Cambridge University Press, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
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Da: Speedyhen, London, Regno Unito
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Editore: Cambridge University Press, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Lingua: Inglese
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Editore: Cambridge University Press 9/10/2020, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Lingua: Inglese
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Editore: Cambridge University Press, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Lingua: Inglese
Da: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
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Editore: Cambridge University Press, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Lingua: Inglese
Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 60,86
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Editore: Cambridge University Press, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Lingua: Inglese
Da: GreatBookPrices, Columbia, MD, U.S.A.
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Aggiungi al carrelloCondizione: 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.
Editore: Cambridge University Press, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Lingua: Inglese
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 59,12
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Editore: Cambridge University Press, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Lingua: Inglese
Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 63,50
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Editore: Cambridge University Press, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Lingua: Inglese
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EUR 81,94
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Editore: Cambridge University Press, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 66,53
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Editore: Cambridge University Press, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Lingua: Inglese
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 69,60
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Editore: Cambridge University Press CUP, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Lingua: Inglese
Da: Books Puddle, New York, NY, U.S.A.
EUR 85,92
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Aggiungi al carrelloHardcover. Condizione: Brand New. 517 pages. 9.50x7.00x1.25 inches. In Stock.
Editore: Cambridge University Press, Cambridge, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Lingua: Inglese
Da: CitiRetail, Stevenage, Regno Unito
EUR 65,38
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Aggiungi al carrelloHardcover. Condizione: 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 our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Editore: Cambridge University Press, Cambridge, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Lingua: Inglese
Da: AussieBookSeller, Truganina, VIC, Australia
EUR 86,26
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Aggiungi al carrelloHardcover. Condizione: 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 our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Editore: Cambridge University Press, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Lingua: Inglese
Da: Lucky's Textbooks, Dallas, TX, U.S.A.
EUR 52,68
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Editore: Cambridge University Press, Cambridge, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Lingua: Inglese
Da: Grand Eagle Retail, Fairfield, OH, U.S.A.
EUR 64,84
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Aggiungi al carrelloHardcover. Condizione: 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.
Da: Revaluation Books, Exeter, Regno Unito
EUR 56,53
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Aggiungi al carrelloHardcover. Condizione: Brand New. 517 pages. 9.50x7.00x1.25 inches. In Stock. This item is printed on demand.
Editore: Cambridge University Press, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Lingua: Inglese
Da: THE SAINT BOOKSTORE, Southport, Regno Unito
EUR 59,14
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Editore: Cambridge University Press, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Lingua: Inglese
Da: Majestic Books, Hounslow, Regno Unito
EUR 85,95
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Editore: Cambridge University Press, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Lingua: Inglese
Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 88,29
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