Edité par Cambridge University Press, 2020
ISBN 10 : 1108486827 ISBN 13 : 9781108486828
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Edité par Cambridge University Press, 2020
ISBN 10 : 1108486827 ISBN 13 : 9781108486828
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Edité par Cambridge University Press, 2020
ISBN 10 : 1108486827 ISBN 13 : 9781108486828
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Edité par Cambridge University Press, 2020
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Edité par Cambridge University Press 9/10/2020, 2020
ISBN 10 : 1108486827 ISBN 13 : 9781108486828
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Ajouter au panierHardback or Cased Book. Etat : New. Bandit Algorithms 2.3. Book.
Edité par Cambridge University Press, 2020
ISBN 10 : 1108486827 ISBN 13 : 9781108486828
Langue: anglais
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Edité par Cambridge University Press, 2020
ISBN 10 : 1108486827 ISBN 13 : 9781108486828
Langue: anglais
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Ajouter au panierEtat : 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.
Edité par Cambridge University Press, 2020
ISBN 10 : 1108486827 ISBN 13 : 9781108486828
Langue: anglais
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Edité par Cambridge University Press, 2020
ISBN 10 : 1108486827 ISBN 13 : 9781108486828
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Edité par Cambridge University Press, 2020
ISBN 10 : 1108486827 ISBN 13 : 9781108486828
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Edité par Cambridge University Press, 2020
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Edité par Cambridge University Press, 2020
ISBN 10 : 1108486827 ISBN 13 : 9781108486828
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Edité par Cambridge University Press CUP, 2020
ISBN 10 : 1108486827 ISBN 13 : 9781108486828
Langue: anglais
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Edité par Cambridge University Press, Cambridge, 2020
ISBN 10 : 1108486827 ISBN 13 : 9781108486828
Langue: anglais
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Ajouter au panierHardcover. Etat : 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.
EUR 84,51
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Ajouter au panierHardcover. Etat : Brand New. 517 pages. 9.50x7.00x1.25 inches. In Stock.
Edité par Cambridge University Press, Cambridge, 2020
ISBN 10 : 1108486827 ISBN 13 : 9781108486828
Langue: anglais
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Ajouter au panierHardcover. Etat : 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.
Edité par Cambridge University Press, 2020
ISBN 10 : 1108486827 ISBN 13 : 9781108486828
Langue: anglais
Vendeur : Lucky's Textbooks, Dallas, TX, Etats-Unis
EUR 52,68
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Edité par Cambridge University Press, Cambridge, 2020
ISBN 10 : 1108486827 ISBN 13 : 9781108486828
Langue: anglais
Vendeur : Grand Eagle Retail, Fairfield, OH, Etats-Unis
EUR 64,84
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Ajouter au panierHardcover. Etat : 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.
Vendeur : Revaluation Books, Exeter, Royaume-Uni
EUR 56,53
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Ajouter au panierHardcover. Etat : Brand New. 517 pages. 9.50x7.00x1.25 inches. In Stock. This item is printed on demand.
Edité par Cambridge University Press, 2020
ISBN 10 : 1108486827 ISBN 13 : 9781108486828
Langue: anglais
Vendeur : THE SAINT BOOKSTORE, Southport, Royaume-Uni
EUR 59,14
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Ajouter au panierHardback. Etat : New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 1220.
Edité par Cambridge University Press, 2020
ISBN 10 : 1108486827 ISBN 13 : 9781108486828
Langue: anglais
Vendeur : Majestic Books, Hounslow, Royaume-Uni
EUR 85,95
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Edité par Cambridge University Press, 2020
ISBN 10 : 1108486827 ISBN 13 : 9781108486828
Langue: anglais
Vendeur : Biblios, Frankfurt am main, HESSE, Allemagne
EUR 88,29
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