| 000 -LEADER |
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| fixed length control field |
nam a22 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
230904b xxu||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781108486828 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
519.3 |
| Item number |
LAT |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Lattimore, Tor |
| 245 ## - TITLE STATEMENT |
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| Title |
Bandit algorithms |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Name of publisher, distributor, etc |
Cambridge University Press, |
| Date of publication, distribution, etc |
2020 |
| Place of publication, distribution, etc |
Cambridge : |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xviii, 518 p. ; |
| Other physical details |
ill., |
| Dimensions |
24 cm |
| 365 ## - TRADE PRICE |
|---|
| Price amount |
41.99 |
| Price type code |
GBP |
| Unit of pricing |
110.40 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc |
Includes bibliographical references and index. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc |
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. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Resource allocation |
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| Topical term or geographic name as entry element |
Mathematical models |
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| Topical term or geographic name as entry element |
Algorithms |
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| Topical term or geographic name as entry element |
Computer algorithms |
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| Topical term or geographic name as entry element |
Decision making |
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| Topical term or geographic name as entry element |
Mathematical optimization |
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| Topical term or geographic name as entry element |
Probabilities |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Szepesvari, Csaba |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
|
| Item type |
Books |