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Volumn , Issue , 2009, Pages 1369-1375

Multiple information sources cooperative learning

Author keywords

[No Author keywords available]

Indexed keywords

LEARNING ALGORITHMS; MACHINE LEARNING; SUPERVISED LEARNING;

EID: 78751690500     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (10)

References (15)
  • 2
    • 0010644122 scopus 로고    scopus 로고
    • Theoretical models of learning to learn
    • T. Mitchell and S. Thrun
    • J. Baxter. Theoretical models of learning to learn. Learning to Learn, T. Mitchell and S. Thrun, 1997.
    • (1997) Learning to Learn
    • Baxter, J.1
  • 3
    • 0242709373 scopus 로고    scopus 로고
    • A theoretical framework for learning from a pool of disparate data sources
    • S. BenDavid, J. Gehrke, and R. Schuller. A theoretical framework for learning from a pool of disparate data sources. In Proc. of KDD, 2002.
    • Proc. of KDD, 2002
    • BenDavid, S.1    Gehrke, J.2    Schuller, R.3
  • 4
    • 0002993682 scopus 로고    scopus 로고
    • Combining labeled and unlabeled data with co-training
    • A. Blum and T. Mitchell. Combining labeled and unlabeled data with co-training. Proc. of COLT, 1998.
    • Proc. of COLT, 1998
    • Blum, A.1    Mitchell, T.2
  • 5
    • 0031189914 scopus 로고    scopus 로고
    • Multitask learning
    • R. Caruana. Multitask learning. In Machine Learning, pages 28:41-75, 1997.
    • (1997) Machine Learning , vol.28 , pp. 41-75
    • Caruana, R.1
  • 6
    • 0000666461 scopus 로고    scopus 로고
    • Data integration using similarity joins and a word-based information representation language
    • W. Cohen. Data integration using similarity joins and a word-based information representation language. In ACM TOIS, pages 18(3):288-321, 2000.
    • (2000) ACM TOIS , vol.18 , Issue.3 , pp. 288-321
    • Cohen, W.1
  • 8
    • 36849087435 scopus 로고    scopus 로고
    • A framework for simultaneous co-clustering and learning from complex data
    • M. Deodhar and J. Ghosh. A framework for simultaneous co-clustering and learning from complex data. In Proc. of KDD, 2007.
    • Proc. of KDD, 2007
    • Deodhar, M.1    Ghosh, J.2
  • 10
    • 34848837283 scopus 로고    scopus 로고
    • Classification of heterogeneous microarray data by maximum entropy kernel
    • W. Fujibuchi and T. Kato. Classification of heterogeneous microarray data by maximum entropy kernel. In BMC:Bioinformatics, page 8, 2007.
    • (2007) BMC:Bioinformatics , pp. 8
    • Fujibuchi, W.1    Kato, T.2
  • 13
    • 84880884400 scopus 로고    scopus 로고
    • Probabilistic classification and clustering in relational data
    • B. Taskar, E. Segal, and D. Koller. Probabilistic classification and clustering in relational data. In Proc. of IJCAI, 2001.
    • Proc. of IJCAI, 2001
    • Taskar, B.1    Segal, E.2    Koller, D.3
  • 15
    • 1942484424 scopus 로고    scopus 로고
    • Eliminating class noise in large datasets
    • Xingquan Zhu and XindongWu. Eliminating class noise in large datasets. Proc. of ICML, 2003.
    • Proc. of ICML, 2003
    • Zhu, X.1    Wu, X.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.