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Volumn , Issue , 2007, Pages 119-126

Laplacian optimal design for image retrieval

Author keywords

Active learning; Experimental design; Image retrieval; Regression; Relevance feedback

Indexed keywords

ACTIVE LEARNING ALGORITHMS; EXPERIMENTAL DESIGN; LAPLACIAN OPTIMAL DESIGNS; OPTIMAL DESIGNS; RELEVANCE FEEDBACK;

EID: 36448966739     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1277741.1277764     Document Type: Article
Times cited : (40)

References (16)
  • 2
    • 33750729556 scopus 로고    scopus 로고
    • Manifold regularizaron: A geometric framework for learning from examples
    • M. Belkin, P. Niyogi, and V. Sindhwani. Manifold regularizaron: A geometric framework for learning from examples. Journal of Machine Learning Research, 7:2399-2434, 2006.
    • (2006) Journal of Machine Learning Research , vol.7 , pp. 2399-2434
    • Belkin, M.1    Niyogi, P.2    Sindhwani, V.3
  • 8
    • 13444266062 scopus 로고    scopus 로고
    • Incremental semi-supervised subspace learning for image retrieval
    • New York, October
    • X. He. Incremental semi-supervised subspace learning for image retrieval. In Proceedings of the ACM Conference on Multimedia, New York, October 2004.
    • (2004) Proceedings of the ACM Conference on Multimedia
    • He, X.1
  • 10
    • 14544275126 scopus 로고    scopus 로고
    • How to complete performance graphs in content-based image retrieval: Add generality and normalize scope
    • D. P. Huijsmans and N. Sebe. How to complete performance graphs in content-based image retrieval: Add generality and normalize scope. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(2):245-251, 2005.
    • (2005) IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.27 , Issue.2 , pp. 245-251
    • Huijsmans, D.P.1    Sebe, N.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.