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Volumn , Issue , 2006, Pages 3910-3913

A multiobjective genetic data inflation methodology for support vector machine classification

Author keywords

Genetic algorithms; Limited training samples problems; Supervised classification; Support vector machines

Indexed keywords

CHROMOSOMES; IMAGE ANALYSIS; OPTIMIZATION; PROBLEM SOLVING; STATISTICAL METHODS; SUPERVISED LEARNING; SUPPORT VECTOR MACHINES;

EID: 34948893478     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IGARSS.2006.1007     Document Type: Conference Paper
Times cited : (3)

References (10)
  • 3
    • 4344614511 scopus 로고    scopus 로고
    • Classification of Hyperspectral Remote Sensing Images With Support Vector Machines
    • PP
    • F. Melgani, L. Bruzzone, "Classification of Hyperspectral Remote Sensing Images With Support Vector Machines", IEEE Trans. Geosci. Remote Sensing vol. 2, PP.1402-1405, 2004.
    • (2004) IEEE Trans. Geosci. Remote Sensing , vol.2 , pp. 1402-1405
    • Melgani, F.1    Bruzzone, L.2
  • 4
    • 0030287048 scopus 로고    scopus 로고
    • The expectation-maximization algorithm
    • T.K. Moon, "The expectation-maximization algorithm", IEEE Signal Processing Mag., pp. 47-59, 1996.
    • (1996) IEEE Signal Processing Mag , pp. 47-59
    • Moon, T.K.1
  • 5
    • 33644859269 scopus 로고    scopus 로고
    • Cell algorithms with data inflation for non-parametric classification
    • A. Palau, F. Melgani, S.B. Serpico, "Cell algorithms with data inflation for non-parametric classification", Pattern Recognition Letters, vol. 27, pp. 781-790, 2006.
    • (2006) Pattern Recognition Letters , vol.27 , pp. 781-790
    • Palau, A.1    Melgani, F.2    Serpico, S.B.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.