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Volumn , Issue , 2006, Pages 324-331

Structured priors for structure learning

Author keywords

[No Author keywords available]

Indexed keywords

BAYES NET; BAYES NET STRUCTURE LEARNING; CLASS ASSIGNMENTS; DATA SETS; DIRECTED ACYCLIC GRAPHS; GENERATIVE MODEL; GRAPH STRUCTURES; HIERARCHICAL BAYESIAN; NON-PARAMETRIC; PRIOR KNOWLEDGE; PRIOR PROBABILITY; REAL-WORLD SYSTEM; SMALL DATA SET; STRUCTURE-LEARNING; SYSTEMATICITY;

EID: 79958774082     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (44)

References (17)
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  • 3
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  • 4
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    • Improving Markov chain Monte Carlo model search for data mining
    • P. Giudici and R. Castelo. Improving Markov Chain Monte Carlo Model Search for Data Mining. Machine Learning, 50:127-158, 2003.
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    • Giudici, P.1    Castelo, R.2
  • 8
    • 0012751948 scopus 로고    scopus 로고
    • Learning bayes net structure from sparse data sets
    • UC Berkeley
    • K. Murphy. Learning bayes net structure from sparse data sets. Technical report, Comp. Sci. Div., UC Berkeley, 2001.
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    • Murphy, K.1
  • 9
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    • Learning Bayesian network parameters from small data sets: Application of Noisy-OR gates
    • DOI 10.1016/S0888-613X(01)00039-1, PII S0888613X01000391
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    • (2001) International Journal of Approximate Reasoning , vol.27 , Issue.2 , pp. 165-182
    • Onisko, A.1    Druzdzel, M.J.2    Wasyluk, H.3
  • 10
    • 33646338193 scopus 로고    scopus 로고
    • MinReg: A scalable algorithm for learning parsimonious regulatory networks in yeast and mammals
    • D. Pe'er, A. Tanay, and A. Regev. MinReg: A Scalable Algorithm for Learning Parsimonious Regulatory Networks in Yeast and Mammals. Journal of Machine Learning Research, 7:167-189, 2006. (Pubitemid 43668127)
    • (2006) Journal of Machine Learning Research , vol.7 , pp. 167-189
    • Pe'er, D.1    Tanay, A.2    Regev, A.3
  • 12
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    • The infinite Gaussian mixture model
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    • Ordering-based search: A simple and effective algorithm for learning Bayesian networks
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.