東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁 到查詢結果 [ subject:"Reinforcement learning." ]

切換: 標籤 | MARC模式 | ISBD

Multi-armed bandits = theory and app...

Zhao, Qing ((Ph.D. in electrical engineering),)

FindBook

Google Book

Amazon

博客來

Multi-armed bandits = theory and applications to online learning in networks /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Multi-armed bandits/ Qing Zhao.
其他題名:	theory and applications to online learning in networks /
其他題名:	Theory and applications to online learning in networks
作者:	Zhao, Qing
面頁冊數:	1 online resource (167 p.)
標題:	Machine learning. -
電子資源:	https://portal.igpublish.com/iglibrary/search/MCPB0006505.html
ISBN:	9781627056380

Multi-armed bandits = theory and applications to online learning in networks /
Zhao, Qing(Ph.D. in electrical engineering),

Multi-armed banditstheory and applications to online learning in networks /[electronic resource] :Theory and applications to online learning in networksQing Zhao. - 1 online resource (167 p.) - Synthesis lectures on communication networks ;22. - Synthesis lectures on communication networks ;22..

Includes bibliographical references (pages 127-145).

Multi-armed bandit problems pertain to optimal sequential decision making and learning in unknown environments. Since the first bandit problem posed by Thompson in 1933 for the application of clinical trials, bandit problems have enjoyed lasting attention from multiple research communities and have found a wide range of applications across diverse domains. This book covers classic results and recent development on both Bayesian and frequentist bandit problems. We start in Chapter 1 with a brief overview on the history of bandit problems, contrasting the two schools--Bayesian and frequentist--of approaches and highlighting foundational results and key applications. Chapters 2 and 4 cover, respectively, the canonical Bayesian and frequentist bandit models. In Chapters 3 and 5, we discuss major variants of the canonical bandit models that lead to new directions, bring in new techniques, and broaden the applications of this classical problem. In Chapter 6, we present several representative application examples in communication networks and social-economic systems, aiming to illuminate the connections between the Bayesian and the frequentist formulations of bandit problems and how structural results pertaining to one may be leveraged to obtain solutions under the other.

Mode of access: World Wide Web.

ISBN: 9781627056380Subjects--Topical Terms:

533906
Machine learning.
Index Terms--Genre/Form:

542853
Electronic books.

LC Class. No.: Q325.5

Dewey Class. No.: 006.3/1

Multi-armed bandits = theory and applications to online learning in networks /
LDR:02341nmm a2200289 i 4500 001 2247708
006 m eo d
007 cr cn |||m|||a
008 211227s2019 cau ob 000 0 eng d
020 $a 9781627056380
020 $a 9781627058711
020 $a 9781681736372
035 $a MCPB0006505
040 $a iG Publishing $b eng $c iG Publishing $e rda
050 0 0 $a Q325.5
082 0 0 $a 006.3/1
100 1 $a Zhao, Qing $c (Ph.D. in electrical engineering), $e author. $3 3512115
245 1 0 $a Multi-armed bandits $h [electronic resource] : $b theory and applications to online learning in networks / $c Qing Zhao.
246 3 0 $a Theory and applications to online learning in networks
264 1 $a [San Rafael, California] : $b Morgan & Claypool Publishers, $c 2019.
300 $a 1 online resource (167 p.)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
490 1 $a Synthesis lectures on communication networks ; $v 22
504 $a Includes bibliographical references (pages 127-145).
520 3 $a Multi-armed bandit problems pertain to optimal sequential decision making and learning in unknown environments. Since the first bandit problem posed by Thompson in 1933 for the application of clinical trials, bandit problems have enjoyed lasting attention from multiple research communities and have found a wide range of applications across diverse domains. This book covers classic results and recent development on both Bayesian and frequentist bandit problems. We start in Chapter 1 with a brief overview on the history of bandit problems, contrasting the two schools--Bayesian and frequentist--of approaches and highlighting foundational results and key applications. Chapters 2 and 4 cover, respectively, the canonical Bayesian and frequentist bandit models. In Chapters 3 and 5, we discuss major variants of the canonical bandit models that lead to new directions, bring in new techniques, and broaden the applications of this classical problem. In Chapter 6, we present several representative application examples in communication networks and social-economic systems, aiming to illuminate the connections between the Bayesian and the frequentist formulations of bandit problems and how structural results pertaining to one may be leveraged to obtain solutions under the other.
538 $a Mode of access: World Wide Web.
650 0 $a Machine learning. $3 533906
650 0 $a Reinforcement learning. $3 1006373
655 4 $a Electronic books. $2 lcsh $3 542853
830 0 $a Synthesis lectures on communication networks ; $v 22. $3 3512116
856 4 0 $u https://portal.igpublish.com/iglibrary/search/MCPB0006505.html