메뉴 건너뛰기




Volumn 146, Issue 2, 2003, Pages 129-148

Discovering simple rules in complex data: A meta-learning algorithm and some surprising musical discoveries

Author keywords

Data mining; Ensemble methods; Expressive music performance; Machine learning; Meta learning; Partial models; Rule discovery

Indexed keywords

ALGORITHMS; DATA MINING; HEURISTIC METHODS; LEARNING SYSTEMS;

EID: 0037880429     PISSN: 00043702     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0004-3702(03)00016-X     Document Type: Article
Times cited : (82)

References (32)
  • 3
    • 0038710451 scopus 로고
    • Efficient pruning methods for separate-and-conquer rule learning systems
    • Chambery, France
    • Cohen W. Efficient pruning methods for separate-and-conquer rule learning systems. Proc. IJCAI-93, Chambery, France. 1993;988-994.
    • (1993) Proc. IJCAI-93 , pp. 988-994
    • Cohen, W.1
  • 6
    • 0002825820 scopus 로고    scopus 로고
    • Automatic extraction of tempo and neat from expressive performances
    • Dixon S. Automatic extraction of tempo and neat from expressive performances. J. New Music Res. 30(1):2001;39-58.
    • (2001) J. New Music Res. , vol.30 , Issue.1 , pp. 39-58
    • Dixon, S.1
  • 8
    • 0030216565 scopus 로고    scopus 로고
    • Unifying instance-based and rule-based induction
    • Domingos P. Unifying instance-based and rule-based induction. Machine Learning. 24:1996;141-168.
    • (1996) Machine Learning , vol.24 , pp. 141-168
    • Domingos, P.1
  • 9
    • 0002426982 scopus 로고    scopus 로고
    • Knowledge discovery via multiple models
    • Domingos P. Knowledge discovery via multiple models. Intelligent Data Analysis. 2:1998;187-202.
    • (1998) Intelligent Data Analysis , vol.2 , pp. 187-202
    • Domingos, P.1
  • 10
    • 0033075882 scopus 로고    scopus 로고
    • Separate-and-conquer rule learning
    • Fürnkranz J. Separate-and-conquer rule learning. Artificial Intelligence Rev. 13(1):1999;3-54.
    • (1999) Artificial Intelligence Rev. , vol.13 , Issue.1 , pp. 3-54
    • Fürnkranz, J.1
  • 14
    • 0014129195 scopus 로고
    • Hierarchical clustering schemes
    • Johnson S.C. Hierarchical clustering schemes. Psychometrika. 2:1967;241-254.
    • (1967) Psychometrika , vol.2 , pp. 241-254
    • Johnson, S.C.1
  • 15
    • 0026459988 scopus 로고
    • Drug design by machine learning: The use of inductive logic programming to model the structure-activity relationship of trimethoprim analogues binding to dihydrofolate reductase
    • King R.D., Muggleton S., Lewis R.A., Sternberg M.J.E. Drug design by machine learning: The use of inductive logic programming to model the structure-activity relationship of trimethoprim analogues binding to dihydrofolate reductase. Proc. Nat. Acad. Sci. 89:1992;11322-11326.
    • (1992) Proc. Nat. Acad. Sci. , vol.89 , pp. 11322-11326
    • King, R.D.1    Muggleton, S.2    Lewis, R.A.3    Sternberg, M.J.E.4
  • 16
    • 0026492833 scopus 로고
    • Protein secondary structure prediction using logic-based machine learning
    • Muggleton S., King R.D., Sternberg M.J.E. Protein secondary structure prediction using logic-based machine learning. Protein Engineering. 5(7):1992;647-657.
    • (1992) Protein Engineering , vol.5 , Issue.7 , pp. 647-657
    • Muggleton, S.1    King, R.D.2    Sternberg, M.J.E.3
  • 17
    • 85101511266 scopus 로고    scopus 로고
    • Analysis and visualization of classifier performance: Comparison under imprecise class and cost distributions
    • Newport Beach, CA
    • Provost F.J., Fawcett T. Analysis and visualization of classifier performance: Comparison under imprecise class and cost distributions. Proc. KDD-97, Newport Beach, CA. 1997;43-48.
    • (1997) Proc. KDD-97 , pp. 43-48
    • Provost, F.J.1    Fawcett, T.2
  • 18
    • 0035283313 scopus 로고    scopus 로고
    • Robust classification for imprecise environments
    • Provost F.J., Fawcett T. Robust classification for imprecise environments. Machine Learning. 42(3):2001;203-231.
    • (2001) Machine Learning , vol.42 , Issue.3 , pp. 203-231
    • Provost, F.J.1    Fawcett, T.2
  • 19
    • 0001172265 scopus 로고
    • Learning logical definitions from relations
    • Quinlan J.R. Learning logical definitions from relations. Machine Learning. 5:1990;239-266.
    • (1990) Machine Learning , vol.5 , pp. 239-266
    • Quinlan, J.R.1
  • 22
    • 0013299558 scopus 로고
    • How can music be expressive?
    • Sundberg J. How can music be expressive? Speech Comm. 13:1993;239-253.
    • (1993) Speech Comm. , vol.13 , pp. 239-253
    • Sundberg, J.1
  • 23
    • 0029272645 scopus 로고
    • Machine discovery in chemistry: New results
    • Valdés-Pérez R.E. Machine discovery in chemistry: New results. Artificial Intelligence. 74:(1):1995;191-201.
    • (1995) Artificial Intelligence , vol.74 , Issue.1 , pp. 191-201
    • Valdés-Pérez, R.E.1
  • 24
    • 0030124957 scopus 로고    scopus 로고
    • A new theorem in particle physics enabled by machine discovery
    • Valdés-Pérez R.E. A new theorem in particle physics enabled by machine discovery. Artificial Intelligence. 82:(1-2):1996;331-339.
    • (1996) Artificial Intelligence , vol.82 , Issue.1-2 , pp. 331-339
    • Valdés-Pérez, R.E.1
  • 25
    • 0033076933 scopus 로고    scopus 로고
    • Principles of human-computer collaboration for knowledge discovery in science
    • Valdés-Pérez R.E. Principles of human-computer collaboration for knowledge discovery in science. Artificial Intelligence. 107:(2):1999;335-346.
    • (1999) Artificial Intelligence , vol.107 , Issue.2 , pp. 335-346
    • Valdés-Pérez, R.E.1
  • 26
    • 0000846313 scopus 로고
    • Rule-based machine learning methods for functional prediction
    • Weiss S., Indurkhya N. Rule-based machine learning methods for functional prediction. J. Artificial Intelligence Res. 3:1995;383-403.
    • (1995) J. Artificial Intelligence Res. , vol.3 , pp. 383-403
    • Weiss, S.1    Indurkhya, N.2
  • 27
    • 0001192557 scopus 로고
    • Combining knowledge-based and instance-based learning to exploit qualitative knowledge
    • Widmer G. Combining knowledge-based and instance-based learning to exploit qualitative knowledge. Informatica. 17:1993;371-385.
    • (1993) Informatica , vol.17 , pp. 371-385
    • Widmer, G.1
  • 28
    • 85065380348 scopus 로고    scopus 로고
    • Large-scale induction of expressive performance rules: First quantitative results
    • Berlin, Germany
    • Widmer G. Large-scale induction of expressive performance rules: First quantitative results. Proc. Internat. Computer Music Conference (ICMC-2000), Berlin, Germany. 2000.
    • (2000) Proc. Internat. Computer Music Conference (ICMC-2000)
    • Widmer, G.1
  • 29
    • 0034753595 scopus 로고    scopus 로고
    • Using AI and machine learning to study expressive music performance: Project survey and first report
    • Widmer G. Using AI and Machine Learning to study expressive music performance: Project survey and first report. AI Comm. 14(3):2001;149-162.
    • (2001) AI Comm. , vol.14 , Issue.3 , pp. 149-162
    • Widmer, G.1
  • 30
    • 84949862150 scopus 로고    scopus 로고
    • The musical expression project: A challenge for machine learning and knowledge discovery
    • Freiburg, Germany
    • Widmer G. The musical expression project: A challenge for machine learning and knowledge discovery. Proc. 12th European Conference on Machine Learning (ECML-01), Freiburg, Germany. 2001.
    • (2001) Proc. 12th European Conference on Machine Learning (ECML-01)
    • Widmer, G.1
  • 31
    • 0038372193 scopus 로고    scopus 로고
    • In search of the Horowitz factor: Interim report on a musical discovery project
    • Lübeck, Germany
    • Widmer G. In search of the Horowitz Factor: Interim report on a musical discovery project. Proc. 5th Internat. Conference on Discovery Science (DS-02), Lübeck, Germany. 2002.
    • (2002) Proc. 5th Internat. Conference on Discovery Science (DS-02)
    • Widmer, G.1
  • 32
    • 0038372191 scopus 로고    scopus 로고
    • Machine discoveries: A few simple, robust local expression principles
    • Widmer G. Machine discoveries: A few simple, robust local expression principles. J. New Music Res. 31:(1):2002;37-50.
    • (2002) J. New Music Res. , vol.31 , Issue.1 , pp. 37-50
    • Widmer, G.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.