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Volumn , Issue , 2009, Pages 6214-6217

A frequency based encoding technique for transformation of categorical variables in mixed IVF dataset

Author keywords

[No Author keywords available]

Indexed keywords

ENCODING (SYMBOLS); SUPPORT VECTOR MACHINES;

EID: 77951021833     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IEMBS.2009.5334548     Document Type: Conference Paper
Times cited : (24)

References (16)
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  • 2
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    • A hybrid neural network with fuzzy rules for categorical and numeric input
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    • Brouwer, R.1
  • 5
    • 38049033442 scopus 로고    scopus 로고
    • Predicting HIV protease-cleavable peptides by discrete support vector machines
    • C. Orsenigo and C. Vercellis, "Predicting HIV protease-cleavable peptides by discrete support vector machines," in EvoBIO, 2007.
    • (2007) EvoBIO
    • Orsenigo, C.1    Vercellis, C.2
  • 6
    • 27944434553 scopus 로고    scopus 로고
    • Clustering observations using fuzzy similarities between ordered categorical data
    • T. Ninomiya, "Clustering observations using fuzzy similarities between ordered categorical data," in Systems, Man and Cybernetics, IEEE Int. Conf. on, 2005.
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    • Ninomiya, T.1
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    • Sequential learning with LS-SVM for large-scale data sets
    • T. Jung and D. Polani, "Sequential learning with LS-SVM for large-scale data sets," in ICANN, 2006.
    • (2006) ICANN
    • Jung, T.1    Polani, D.2
  • 11
    • 34247527796 scopus 로고    scopus 로고
    • Prolonged duration of transfer does not affect outcome in cycles with good embryo quality
    • H. N. Ciray, S. Tosun, O. Hacifazlioglu, A. Mesut, and M. Bahceci, "Prolonged duration of transfer does not affect outcome in cycles with good embryo quality," Fertil. Steril., 2007.
    • (2007) Fertil. Steril.
    • Ciray, H.N.1    Tosun, S.2    Hacifazlioglu, O.3    Mesut, A.4    Bahceci, M.5
  • 14
    • 27144489164 scopus 로고    scopus 로고
    • A Tutorial on Support Vector Machines for Pattern Recognition
    • C. Burges, "A Tutorial on Support Vector Machines for Pattern Recognition," Data Mining and Knowledge Discovery, vol.2, no.2, pp. 121-167, 1998.
    • (1998) Data Mining and Knowledge Discovery , vol.2 , Issue.2 , pp. 121-167
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    • Benchmarking classification models for software defect prediction: A proposed framework and novel findings
    • S. Lessmann, B. Baesens, C. Mues, and S. Pietsch, "Benchmarking classification models for software defect prediction: A proposed framework and novel findings," IEEE Trans. on Software Engineering, vol.34, pp. 485-496, 2008.
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    • Lessmann, S.1    Baesens, B.2    Mues, C.3    Pietsch, S.4
  • 16
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    • Learning when data sets are imbalanced and when costs are unequal and unknown
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.