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Volumn 13, Issue 3, 1999, Pages 257-272

Evaluating machine learning models for engineering problems

Author keywords

[No Author keywords available]

Indexed keywords

LEARNING SYSTEMS; MATHEMATICAL MODELS; NEURAL NETWORKS; STATISTICAL TESTS;

EID: 0032628293     PISSN: 09541810     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0954-1810(98)00021-1     Document Type: Article
Times cited : (156)

References (36)
  • 1
    • 0031188065 scopus 로고    scopus 로고
    • Machine learning techniques for civil engineering problems
    • Reich Y. Machine learning techniques for civil engineering problems. Microcomputers in Civil Engineering. 12(4):1997;307-322.
    • (1997) Microcomputers in Civil Engineering , vol.124 , pp. 307-322
    • Reich, Y.1
  • 4
    • 0001942829 scopus 로고
    • Neural networks and the bias/variance dilemma
    • Geman S., Bienstock E., Doursat R. Neural networks and the bias/variance dilemma. Neural Computation. 4:(1):1992;1-58.
    • (1992) Neural Computation , vol.4 , Issue.1 , pp. 1-58
    • Geman, S.1    Bienstock, E.2    Doursat, R.3
  • 6
    • 0001213986 scopus 로고
    • The design and analysis of pattern recognition experiments
    • Highleyman W. The design and analysis of pattern recognition experiments. Bell System Technical Journal. 41:(1962):1962;723-744.
    • (1962) Bell System Technical Journal , vol.41 , Issue.1962 , pp. 723-744
    • Highleyman, W.1
  • 8
    • 0000459353 scopus 로고    scopus 로고
    • The lack of a priori distinctions between learning algorithms
    • Wolpert D.H. The lack of a priori distinctions between learning algorithms. Neural Computation. 8:1996;1341-1390.
    • (1996) Neural Computation , vol.8 , pp. 1341-1390
    • Wolpert, D.H.1
  • 12
    • 0008411625 scopus 로고    scopus 로고
    • Statistical tests for comparing supervized classification learning algorithms
    • Department of Computer Science, Oregon State University
    • Dietterich TG. Statistical tests for comparing supervized classification learning algorithms. Technical report, Department of Computer Science, Oregon State University, 1996.
    • (1996) Technical Report
    • Dietterich, T.G.1
  • 13
    • 84950461478 scopus 로고
    • Estimating the error rate of a prediction rule: Improvement on cross-validation
    • Efron B. Estimating the error rate of a prediction rule: Improvement on cross-validation. Journal of the American Statistical Association. 78:(382):1983;316-331.
    • (1983) Journal of the American Statistical Association , vol.78 , Issue.382 , pp. 316-331
    • Efron, B.1
  • 15
    • 0012320945 scopus 로고
    • Statistical evaluation of neural network experiments: Minimum requirements and current practice
    • In Trappl R, editor, Vienna, Austria, Austrian Society for Cybernetic Studies
    • Flexer A. Statistical evaluation of neural network experiments: minimum requirements and current practice. In Trappl R, editor, Proceedings of the Thirteenth European Meeting on Cybernetics and Systems Research, 1995, pp. 1005-1008, Vienna, Austria, Austrian Society for Cybernetic Studies.
    • (1995) Proceedings of the Thirteenth European Meeting on Cybernetics and Systems Research , pp. 1005-1008
    • Flexer, A.1
  • 17
    • 0030344230 scopus 로고    scopus 로고
    • Heuristics of instability and stabilization
    • Breiman L. Heuristics of instability and stabilization. The Annals of Statistics. 24:(6):1996;2350-2383.
    • (1996) The Annals of Statistics , vol.24 , Issue.6 , pp. 2350-2383
    • Breiman, L.1
  • 18
    • 0001777104 scopus 로고
    • Methods of comparison
    • D. Michie, D. Spiegelhalter, & C.C. Taylor. Chichester, England: Ellis Horwood Publishers
    • Henry R.J. Methods of comparison. Michie D., Spiegelhalter D., Taylor C.C. Machine Learning, Neural and Statistical Classification. 1994;107-124 Ellis Horwood Publishers, Chichester, England.
    • (1994) Machine Learning, Neural and Statistical Classification , pp. 107-124
    • Henry, R.J.1
  • 20
    • 0001512820 scopus 로고
    • An empirical comparison of pattern recognition, neural nets, and machine learning classification methods
    • Detroit, MI, San Mateo, CA, Morgan Kaufmann
    • Weiss SM, Kapouleas I. An empirical comparison of pattern recognition, neural nets, and machine learning classification methods. In Proceedings of The Eleventh International Joint Conference on Artificial Intelligence, Detroit, MI, San Mateo, CA, Morgan Kaufmann, 1989, pp. 781-787.
    • (1989) In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence , pp. 781-787
    • Weiss, S.M.1    Kapouleas, I.2
  • 21
  • 24
  • 25
    • 0017336301 scopus 로고
    • Asymptotics for and against cross-validation
    • Stone M. Asymptotics for and against cross-validation. Biometrika. 64(1):1977;29-35.
    • (1977) Biometrika , vol.641 , pp. 29-35
    • Stone, M.1
  • 30
    • 0029520857 scopus 로고
    • Stress corrosion cracking of sensitized type 304 stainless steel in doped high-temperature water
    • Congleton J., Berrisford R., Yang W. Stress corrosion cracking of sensitized type 304 stainless steel in doped high-temperature water. Corrosion Science. 51:(12):1995;901-910.
    • (1995) Corrosion Science , vol.51 , Issue.12 , pp. 901-910
    • Congleton, J.1    Berrisford, R.2    Yang, W.3
  • 32
    • 0030130727 scopus 로고    scopus 로고
    • A quantitative study in experimental evaluation of neural network learning algorithms: Current research practice
    • Prechelt L. A quantitative study in experimental evaluation of neural network learning algorithms: Current research practice. Neural Networks. 9:(3):1996;457-462.
    • (1996) Neural Networks , vol.9 , Issue.3 , pp. 457-462
    • Prechelt, L.1
  • 35
    • 84870267223 scopus 로고
    • The generalization of Student's problem when several different population variances are involved
    • Welch B.L. The generalization of Student's problem when several different population variances are involved. Biometrika. 34:1947;28-35.
    • (1947) Biometrika , vol.34 , pp. 28-35
    • Welch, B.L.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.