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Volumn 29, Issue 3, 1998, Pages 263-282

Concept representation with overlapping feature intervals

Author keywords

[No Author keywords available]

Indexed keywords

LEARNING ALGORITHMS; LEARNING SYSTEMS;

EID: 0032043030     PISSN: 01969722     EISSN: 10876553     Source Type: Journal    
DOI: 10.1080/019697298125713     Document Type: Article
Times cited : (5)

References (14)
  • 1
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    • Tolerating noisy, irrelevant and novel attributes in instance-based learning algorithms
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    • (1992) Int. J. Man -Machine Studies , vol.36 , pp. 267-287
    • Aha, D.W.1
  • 2
    • 0025725905 scopus 로고
    • Instance-based learning algorithms
    • Aha, D. W., D. Kibler, and M. K. Albert. 1991. Instance-based learning algorithms. Mach. Learn6:37-66.
    • (1991) Mach. Learn , vol.6 , pp. 37-66
    • Aha, D.W.1    Kibler, D.2    Albert, M.K.3
  • 3
    • 0010455575 scopus 로고    scopus 로고
    • K nearest neighbor classification on feature projections
    • L. Saitta, Morgan Kaufmann. San Mateo, CA: Italy, July
    • Akkus, A., and H. A. Guvenir. 1996. K nearest neighbor classification on feature projections. Proceedings International Conference on Machine Learn ing, ICML’96, ed. L. Saitta, pp. 12-19. Morgan Kaufmann. San Mateo, CA: Italy, July.
    • (1996) Proceedings International Conference on Machine Learn ing, ICML’96 , pp. 12-19
    • Akkus, A.1    Guvenir, H.A.2
  • 6
    • 0030130153 scopus 로고    scopus 로고
    • Classification by feature partitioning
    • Guvenir, H. A., and I. Sirin. 1996. Classification by feature partitioning. Mach. Learn. 23:47-67.
    • (1996) Mach. Learn , vol.23 , pp. 47-67
    • Guvenir, H.A.1    Sirin, I.2
  • 7
    • 0027580356 scopus 로고
    • Very simple classification rules perform well on most commonly used datasets
    • Holte, R. C. 1993. Very simple classification rules perform well on most commonly used datasets. Mach. Learn. 11:63-91.
    • (1993) Mach. Learn , vol.11 , pp. 63-91
    • Holte, R.C.1
  • 8
    • 0027682531 scopus 로고
    • Inductive and Bayesian learning in medical diagnosis
    • Kononenko, I. 1993. Inductive and Bayesian learning in medical diagnosis. Appl. Artif. Intell.7:317-337.
    • (1993) Appl. Artif. Intell , vol.7 , pp. 317-337
    • Kononenko, I.1
  • 9
  • 11
    • 0001594860 scopus 로고
    • A general framework for induction and a study of selective induction
    • Rendell, L. 1986. A general framework for induction and a study of selective induction. Mach. Learn. 1:177-226.
    • (1986) Mach. Learn , vol.1 , pp. 177-226
    • Rendell, L.1
  • 12
    • 0026156490 scopus 로고
    • A nearest hyperrectangle learning method
    • Salzberg, S. 1991. A nearest hyperrectangle learning method. Mach. Learn. 6:251-276.
    • (1991) Mach. Learn , vol.6 , pp. 251
    • Salzberg, S.1
  • 13
    • 85152519885 scopus 로고
    • An improved algorithm for incremental induction of decision trees
    • New Brunswick, NJ: Morgan Kaufmann
    • Utgoff, P. E. 1994. An improved algorithm for incremental induction of decision trees. Machine Learning: Proceedings of the Eleventh International Conference, pp. 318-325. New Brunswick, NJ: Morgan Kaufmann.
    • (1994) Machine Learning: Proceedings of the Eleventh International Conference , pp. 318-325
    • Utgoff, P.E.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.