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Volumn 9, Issue , 2010, Pages 509-516

Inductive principles for restricted Boltzmann machine learning

Author keywords

[No Author keywords available]

Indexed keywords

CONTRASTIVE DIVERGENCE; DATA SETS; EMPIRICAL EVALUATIONS; LEARNING METHODS; MAXIMUM LIKELIHOOD LEARNING; MULTIPLE TASKS; PARTITION FUNCTIONS; PSEUDO-LIKELIHOOD; RESTRICTED BOLTZMANN MACHINE;

EID: 80053455323     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper
Times cited : (123)

References (18)
  • 1
    • 0000582521 scopus 로고
    • Statistical analysis of non-lattice data
    • J. Besag. Statistical analysis of non-lattice data. The Statistician, 24(3):179-195, 1975.
    • (1975) The Statistician , vol.24 , Issue.3 , pp. 179-195
    • Besag, J.1
  • 3
    • 0013344078 scopus 로고    scopus 로고
    • Training products of experts by minimizing contrastive divergence
    • G. Hinton. Training products of experts by minimizing contrastive divergence. Neural Computation, 14, 2000.
    • (2000) Neural Computation , vol.14
    • Hinton, G.1
  • 4
    • 34848816179 scopus 로고    scopus 로고
    • To recognize shapes, first learn to generate images
    • G. E. Hinton. To recognize shapes, first learn to generate images. Progress in brain research, 165:535-547, 2007.
    • (2007) Progress in Brain Research , vol.165 , pp. 535-547
    • Hinton, G.E.1
  • 8
    • 80053159994 scopus 로고    scopus 로고
    • Interpretation and generalization of score matching
    • Siwei Lyu. Interpretation and generalization of score matching. In Uncertainty in Artificial Intelligence 25, 2009.
    • (2009) Uncertainty in Artificial Intelligence , vol.25
    • Lyu, S.1
  • 15
    • 56449086223 scopus 로고    scopus 로고
    • Training restricted Boltzmann machines using approximations to the likelihood gradient
    • T. Tieleman. Training restricted Boltzmann machines using approximations to the likelihood gradient. In International Conference on Machine Learning 25, pages 1064-1071, 2008.
    • (2008) International Conference on Machine Learning , vol.25 , pp. 1064-1071
    • Tieleman, T.1
  • 18
    • 0000355193 scopus 로고
    • Parametric inference for imperfectly observed Gibbsian fields
    • L. Younes. Parametric inference for imperfectly observed Gibbsian fields. Probability Theory and Related Fields, 82(4):625-645, 1989.
    • (1989) Probability Theory and Related Fields , vol.82 , Issue.4 , pp. 625-645
    • Younes, L.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.