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Volumn , Issue , 2005, Pages 17-23

Reinforcement learning for active model selection

Author keywords

budgeted learning; data acquisition; learning costs; training costs

Indexed keywords

ACTIVE MODELS; LEARNING COSTS; LEARNING DATA; LEARNING SCENARIOS; MACHINE-LEARNING; MARKOV DECISION PROBLEM; REINFORCEMENT LEARNING TECHNIQUES; TRAINING COSTS; TRAINING DATA;

EID: 77953565591     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1089827.1089829     Document Type: Conference Paper
Times cited : (7)

References (19)
  • 3
    • 33750693259 scopus 로고    scopus 로고
    • The max k-armed bandit: A new model of exploration applied to search heuristic selection
    • V. A. Cicirello and S. F. Smith. The max k-armed bandit: a new model of exploration applied to search heuristic selection. In AAAI, 2005.
    • (2005) AAAI
    • Cicirello, V.A.1    Smith, S.F.2
  • 8
    • 0036832951 scopus 로고    scopus 로고
    • A sparse sampling algorithm for nearoptimal planning in large markov decision processes
    • M. Kearns, Y. Mansour, and A. Y. Ng. A sparse sampling algorithm for nearoptimal planning in large markov decision processes. Machine Learning, 2002.
    • (2002) Machine Learning
    • Kearns, M.1    Mansour, Y.2    Ng, A.Y.3
  • 16
    • 33847202724 scopus 로고
    • Learning to predict by the method of temporal differences
    • R. S. Sutton. Learning to predict by the method of temporal differences. Machine Learning, 1988.
    • (1988) Machine Learning
    • Sutton, R.S.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.