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Volumn 3, Issue 2, 2003, Pages 271-301

On online learning of decision lists

Author keywords

[No Author keywords available]

Indexed keywords

FEATURE EXTRACTION; LEARNING ALGORITHMS; ONLINE SYSTEMS; THEOREM PROVING;

EID: 0041965972     PISSN: 15324435     EISSN: None     Source Type: Journal    
DOI: 10.1162/153244303765208395     Document Type: Article
Times cited : (17)

References (18)
  • 1
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    • Angluin, D.1
  • 4
    • 0026877650 scopus 로고
    • Rank-r decision trees are a subclass of r-decision lists
    • Avrim Blum. Rank-r decision trees are a subclass of r-decision lists. Information Processing Letters, 42(4):183-185, 1992.
    • (1992) Information Processing Letters , vol.42 , Issue.4 , pp. 183-185
    • Blum, A.1
  • 5
    • 0038384383 scopus 로고    scopus 로고
    • On-line algorithms in machine learning
    • Dagstuhl, June
    • Avrim Blum. On-line algorithms in machine learning. In Proceedings of the Work-shop on On-Line Algorithms, Dagstuhl, June 1996. Available electronically at http://www-2.cs.emu.edu/̃avrim
    • (1996) Proceedings of the Work-shop on On-line Algorithms
    • Blum, A.1
  • 6
    • 0029254047 scopus 로고
    • Learning in the presence of finitely or infintely many irrelevant attributes
    • Avrim Blum, Lisa Hellerstein, and Nicholas Littlstone. Learning in the presence of finitely or infintely many irrelevant attributes. Journal of Computer and System Sciences, 50(1) :32-40, 1995.
    • (1995) Journal of Computer and System Sciences , vol.50 , Issue.1 , pp. 32-40
    • Blum, A.1    Hellerstein, L.2    Littlstone, N.3
  • 10
    • 0041494200 scopus 로고    scopus 로고
    • Decision lists and related boolean functions
    • Institute of Informatics, University of Giessen
    • Thomas Eiter, Toshihide Ibaraki, and Kazuhisa Makino. Decision lists and related boolean functions. Research Report 9804, Institute of Informatics, University of Giessen, 1998.
    • (1998) Research Report , vol.9804
    • Eiter, T.1    Ibaraki, T.2    Makino, K.3
  • 12
    • 0025446773 scopus 로고
    • Learning nested differences of intersection closed concept classes
    • David Helmbold, Robert Sloan, and Manfred K. Warmuth. Learning nested differences of intersection closed concept classes. Machine Learning, 5(2): 165-196, 1990.
    • (1990) Machine Learning , vol.5 , Issue.2 , pp. 165-196
    • Helmbold, D.1    Sloan, R.2    Warmuth, M.K.3
  • 13
    • 34250091945 scopus 로고
    • Learning when irrelevant attributes abound: A new linear-threshold algorithm
    • Nicholas Littlestone. Learning when irrelevant attributes abound: a new linear-threshold algorithm. Machine Learning, 2:285-318, 1988.
    • (1988) Machine Learning , vol.2 , pp. 285-318
    • Littlestone, N.1
  • 15
    • 1442267080 scopus 로고
    • Learning decision lists
    • Ronald L. Rivest. Learning decision lists. Machine Learning, 2(3):229-246, 1987.
    • (1987) Machine Learning , vol.2 , Issue.3 , pp. 229-246
    • Rivest, R.L.1
  • 16
    • 0034140159 scopus 로고    scopus 로고
    • Computational sample complexity and attribute efficient learning
    • Rocco A. Servadio. Computational sample complexity and attribute efficient learning. Journal of Computer and System Sciences, 60(1):161-178, 2000.
    • (2000) Journal of Computer and System Sciences , vol.60 , Issue.1 , pp. 161-178
    • Servadio, R.A.1
  • 17
    • 0021518106 scopus 로고
    • A theory of the learnable
    • Leslie G. Valiant. A theory of the learnable. Communications of the ACM, 27(11) :1134-1142, 1984.
    • (1984) Communications of the ACM , vol.27 , Issue.11 , pp. 1134-1142
    • Valiant, L.G.1
  • 18
    • 0033225586 scopus 로고    scopus 로고
    • Projection learning
    • Leslie G. Valiant. Projection learning. Machine Learning, 37(2):115-130, 1999.
    • (1999) Machine Learning , vol.37 , Issue.2 , pp. 115-130
    • Valiant, L.G.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.