The budgeted multi-armed bandit problem

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 3120, Issue , 2004, Pages 643-645

  Madani, Omid ^a   Lizotte, Daniel J ^b   Greiner, Russell ^b  

^a YAHOO RESEARCH   (United States)

^b UNIVERSITY OF ALBERTA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; POLYNOMIALS; PROBABILITY; PROBLEM SOLVING; SET THEORY; APPROXIMATION ALGORITHMS; COMPUTATIONAL EFFICIENCY; STATISTICS;

GREEDY ALGORITHM; MULTI-ARMED BANDIT PROBLEM; ROUND-ROBIN ALGORITHM; CLINICAL TRIAL; EXPLORATION/EXPLOITATION; FIXED NUMBERS; PARAMETER SETTING;

COMPUTATIONAL COMPLEXITY; BUDGET CONTROL;

EID: 9444252980     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-27819-1_46     Document Type: Conference Paper

References (3)

1. [BF85] D. Berry and B. Fristedt. Bandit Problems: Sequential Allocation of Experiments. Chapman and Hall, New York, NY, 1985.
2. [LMG03] D. Lizotte, O. Madani, and R. Greiner. Budgeted learning of Naive Bayes classifiers. In UAI-2003, 2003.
3. [MLG04] O. Madani, D. Lizotte, and R. Greiner. Active model selection (submitted). Technical report, University of Alberta and AICML, 2004. http://www.cs.ualberta.ca/~madani/budget.html.