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Volumn 1, Issue , 2010, Pages 309-314

A comparative framework for evaluating classification algorithms

Author keywords

Classification; Data mining; Model assessment

Indexed keywords

CLASSIFICATION ACCURACY; CLASSIFICATION ALGORITHM; CLASSIFICATION MODELS; DATA MINING METHODS; DATA SETS; LARGE AMOUNTS OF DATA; MODEL ASSESSMENT; REGRESSION MODEL;

EID: 79959846890     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (6)

References (15)
  • 3
    • 0042711018 scopus 로고    scopus 로고
    • On the Need for Time Series Data mining benchmarks: A survey and empirical demonstration
    • E. Keogh, S. Kasetty, "On the Need for Time Series Data mining benchmarks: a survey and empirical demonstration", Data Mining and Knowledge Discovery, Vol. 7, pp. 349-371.
    • Data Mining and Knowledge Discovery , vol.7 , pp. 349-371
    • Keogh, E.1    Kasetty, S.2
  • 7
    • 0001941003 scopus 로고
    • Comparing connectionist and symbolic learning methods
    • S. J. Hanson, G. A. Drastal, and R. L. Rivest, Ed. Cambridge: MIT Press
    • J. R. Quintan, "Comparing connectionist and symbolic learning methods," in Computational Learning Theory and Natural Learning Systems: Constraints and Prospect, vol. 1, S. J. Hanson, G. A. Drastal, and R. L. Rivest, Ed. Cambridge: MIT Press, 1994, pp. 445-446.
    • (1994) Computational Learning Theory and Natural Learning Systems: Constraints and Prospect , vol.1 , pp. 445-446
    • Quintan, J.R.1
  • 9
    • 79959823433 scopus 로고
    • Associative methods in reinforcement learning: An emprical study
    • S. J. Hanson, T. Petsche, M. Kearns and R. L. Rivest, Ed. Cambridge: MIT Press
    • L. P. Kaelbling, "Associative methods in reinforcement learning: an emprical study," in Computational Learning Theory and Natural Learning Systems: Intersection Between Theory and Experiment, vol. 2, S. J. Hanson, T. Petsche, M. Kearns and R. L. Rivest, Ed. Cambridge: MIT Press, 1994, pp. 145-153.
    • (1994) Computational Learning Theory and Natural Learning Systems: Intersection between Theory and Experiment , vol.2 , pp. 145-153
    • Kaelbling, L.P.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.