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Volumn , Issue , 2009, Pages 1819-1822

To obtain orthogonal feature extraction using training data selection

Author keywords

Incremental learning; Orthogonal components

Indexed keywords

DATA SPACE; EFFECTIVE TOOL; EIGEN-VALUE; EIGENVECTORS; FEATURE EXTRACTION METHODS; ILL CONDITIONED PROBLEMS; INCREMENTAL LEARNING; LARGE-COMPUTING; MACHINE-LEARNING; NUMBER OF COMPONENTS; ORTHOGONAL BASIS; ORTHOGONAL COMPONENTS; PRIOR KNOWLEDGE; REDUCTANTS; SELECTION SCHEME; SELF-ADAPTIVE; TRAINING DATA; TRAINING PATTERNS;

EID: 74549176590     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1645953.1646239     Document Type: Conference Paper
Times cited : (8)

References (12)
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  • 6
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    • X. He, D. Cai, and J. Han. Learning a maximum margin subspace for image retrieval. IEEE Trans. Knowledge and Data Engineering, 20(2):189-201, 2008.
    • (2008) IEEE Trans. Knowledge and Data Engineering , vol.20 , Issue.2 , pp. 189-201
    • He, X.1    Cai, D.2    Han, J.3
  • 9
    • 17044405923 scopus 로고    scopus 로고
    • Toward integrating feature selection algorithms for classification and clustering
    • H. Liu and L. Yu. Toward integrating feature selection algorithms for classification and clustering. IEEE Trans. Knowledge and Data Engineering, 17(4):491-502, 2005.
    • (2005) IEEE Trans. Knowledge and Data Engineering , vol.17 , Issue.4 , pp. 491-502
    • Liu, H.1    Yu, L.2
  • 11
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    • Numerical dependence theorem and its application (in chinese)
    • X. He. Numerical dependence theorem and its application (in chinese). Numerical Mathematics, A Journal of Chinese Universities, 1(1):11-19, 1979.
    • (1979) Numerical Mathematics, A Journal of Chinese Universities , vol.1 , Issue.1 , pp. 11-19
    • He, X.1
  • 12
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    • Department of Information
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.