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Volumn , Issue , 2009, Pages 521-524

Semi-supervised topic modeling for image annotation

Author keywords

Automatic image annotation; Laplacian regularization; Semantic indexing; Semi supervised learning

Indexed keywords

AUTOMATIC IMAGE ANNOTATION; EM ALGORITHMS; GENERATIVE MODEL; GRAPH LAPLACIAN; IMAGE ANNOTATION; LABELED IMAGES; LAPLACIANS; NEAREST NEIGHBORS; NOVEL TECHNIQUES; PROBABILISTIC SEMANTICS; PROBABILITY DENSITIES; REGULARIZER; SEMANTIC INDEXING; SEMI-SUPERVISED; SEMI-SUPERVISED LEARNING; TEXTUAL ANNOTATIONS; VISUAL FEATURE;

EID: 72449205488     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1631272.1631346     Document Type: Conference Paper
Times cited : (10)

References (12)
  • 2
    • 33750729556 scopus 로고    scopus 로고
    • Manifold regularization: A geometric framework for learning from labeled and unlabeled examples
    • M. Belkin, P. Niyogi, and V. Sindhwani. Manifold regularization: A geometric framework for learning from labeled and unlabeled 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
  • 10
    • 0002788893 scopus 로고    scopus 로고
    • A view of the em algorithm that justifies incremental, sparse, and other variants
    • R. M. Neal and G. E. Hinton. A view of the em algorithm that justifies incremental, sparse, and other variants. In Learning in graphical models, pages 355-368. 1999.
    • (1999) Learning in graphical models , pp. 355-368
    • Neal, R.M.1    Hinton, G.E.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.