메뉴 건너뛰기




Volumn , Issue PART 1, 2013, Pages 705-713

Thurstonian Boltzmann machines: Learning from multiple inequalities

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; SOFTWARE ENGINEERING;

EID: 84897481062     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (10)

References (36)
  • 1
    • 0014841634 scopus 로고
    • Multi-variate probit analysis
    • JR Ashford and RR Sowden. Multi-variate probit analysis. Biometrics, pages 535-546, 1970.
    • (1970) Biometrics , pp. 535-546
    • Ashford, J.R.1    Sowden, R.R.2
  • 2
    • 34247105853 scopus 로고    scopus 로고
    • Thurstonian-based analyses: Past, present, and future utilities
    • U. Böckenholt. Thurstonian-based analyses: past, present, and future utilities. Psychometrika, 71(4):615-629, 2006.
    • (2006) Psychometrika , vol.71 , Issue.4 , pp. 615-629
    • Böckenholt, U.1
  • 3
    • 74549208546 scopus 로고    scopus 로고
    • Expected reciprocal rank for graded relevance
    • ACM
    • O. Chapelle, D. Metlzer, Y. Zhang, and P. Grinspan. Expected reciprocal rank for graded relevance. In CIKM, pages 621-630. ACM, 2009.
    • (2009) CIKM , pp. 621-630
    • Chapelle, O.1    Metlzer, D.2    Zhang, Y.3    Grinspan, P.4
  • 4
    • 0000911137 scopus 로고    scopus 로고
    • Analysis of multivariate probit models
    • S. Chib and E. Greenberg. Analysis of multivariate probit models. Biometrika, 85(2):347-361, 1998.
    • (1998) Biometrika , vol.85 , Issue.2 , pp. 347-361
    • Chib, S.1    Greenberg, E.2
  • 6
    • 21644435901 scopus 로고    scopus 로고
    • Bayesian latent variable models for mixed discrete outcomes
    • D. B. Dunson and A.H. Herring. Bayesian latent variable models for mixed discrete outcomes. Biostatistics, 6(1): 11, 2005.
    • (2005) Biostatistics , vol.6 , Issue.1 , pp. 11
    • Dunson, D.B.1    Herring, A.H.2
  • 7
    • 0345368881 scopus 로고
    • Unsupervised learning of distributions on binary vectors using two layer networks
    • Y. Freund and D. Haussler. Unsupervised learning of distributions on binary vectors using two layer networks. Advances in Neural Information Processing Systems, pages 912-919, 1993.
    • (1993) Advances in Neural Information Processing Systems , pp. 912-919
    • Freund, Y.1    Haussler, D.2
  • 8
    • 33749243771 scopus 로고    scopus 로고
    • The rate adapting Poisson model for information retrieval and object recognition
    • P. V. Gehler, A.D. Holub, and M. Welling. The rate adapting Poisson model for information retrieval and object recognition. In Proceedings of the ICML, pages 337-344, 2006.
    • (2006) Proceedings of the ICML , pp. 337-344
    • Gehler, P.V.1    Holub, A.D.2    Welling, M.3
  • 9
    • 0001878857 scopus 로고
    • Efficient simulation from the multivariate normal and student-t distributions subject to linear constraints and the evaluation of constraint probabilities
    • J. Geweke. Efficient simulation from the multivariate normal and student-t distributions subject to linear constraints and the evaluation of constraint probabilities. In Computing science and statistics: Proceedings of the 23rd symposium on the interface, pages 571-578, 1991.
    • (1991) Computing Science and Statistics: Proceedings of the 23rd Symposium on the Interface , pp. 571-578
    • Geweke, J.1
  • 10
    • 33746600649 scopus 로고    scopus 로고
    • Reducing the dimensionality of data with neural networks
    • DOI 10.1126/science.1127647
    • G.E. Hinton and R.R. Salakhutdinov. Reducing the dimensionality of data with neural networks. Science, 313 (5786):504-507, 2006. (Pubitemid 44148451)
    • (2006) Science , vol.313 , Issue.5786 , pp. 504-507
    • Hinton, G.E.1    Salakhutdinov, R.R.2
  • 13
    • 26644473169 scopus 로고    scopus 로고
    • Nonparametric Bayesian modeling for multivariate ordinal data
    • DOI 10.1198/106186005X63185
    • A. Kottas, P. Müller, and F. Quintana. Nonparametric Bayesian modeling for multivariate ordinal data. Journal of Computational and Graphical Statistics, 14(3):610-625, 2005. (Pubitemid 41442152)
    • (2005) Journal of Computational and Graphical Statistics , vol.14 , Issue.3 , pp. 610-625
    • Kottas, A.1    Muller, P.2    Quintana, F.3
  • 14
    • 79951571992 scopus 로고    scopus 로고
    • Learning a generative model of images by factoring appearance and shape
    • N. Le Roux, N. Heess, J. Shotton, and J. Winn. Learning a generative model of images by factoring appearance and shape. Neural Computation, 23(3):593-650, 2011.
    • (2011) Neural Computation , vol.23 , Issue.3 , pp. 593-650
    • Le Roux, N.1    Heess, N.2    Shotton, J.3    Winn, J.4
  • 16
    • 77955989954 scopus 로고    scopus 로고
    • Modeling pixel means and covariances using factorized third-order Boltzmann machines
    • IEEE
    • M.A. Ranzato and G.E. Hinton. Modeling pixel means and covariances using factorized third-order Boltzmann machines. In CVPR, pages 2551-2558. IEEE, 2010.
    • (2010) CVPR , pp. 2551-2558
    • Ranzato, M.A.1    Hinton, G.E.2
  • 17
    • 0001153986 scopus 로고
    • Simulation of truncated normal variables
    • C.P. Robert. Simulation of truncated normal variables. Statistics and computing, 5(2):121-125, 1995.
    • (1995) Statistics and Computing , vol.5 , Issue.2 , pp. 121-125
    • Robert, C.P.1
  • 21
    • 78649978910 scopus 로고    scopus 로고
    • List-wise learning to rank with matrix factorization for collaborative filtering
    • ACM
    • Y. Shi, M. Larson, and A. Hanjalic. List-wise learning to rank with matrix factorization for collaborative filtering. In ACM RecSys, pages 269-272. ACM, 2010.
    • (2010) ACM RecSys , pp. 269-272
    • Shi, Y.1    Larson, M.2    Hanjalic, A.3
  • 23
    • 84877724347 scopus 로고    scopus 로고
    • Multimodal learning with deep Boltzmann machines
    • N. Srivastava and R. Salakhutdinov. Multimodal learning with deep Boltzmann machines. In NIPS, pages 2231-2239, 2012.
    • (2012) NIPS , pp. 2231-2239
    • Srivastava, N.1    Salakhutdinov, R.2
  • 24
    • 58149426837 scopus 로고
    • A law of comparative judgment
    • L. L. Thurstone. A law of comparative judgment. Psycho-logical review, 34(4):273, 1927.
    • (1927) Psycho-logical Review , vol.34 , Issue.4 , pp. 273
    • Thurstone, L.L.1
  • 25
    • 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 Proceedings of the 25th ICML, pages 1064-1071, 2008.
    • (2008) Proceedings of the 25th ICML , pp. 1064-1071
    • Tieleman, T.1
  • 32
    • 0035543522 scopus 로고    scopus 로고
    • Factor analysis with (mixed) observed and latent variables in the exponential family
    • M. Wedel and W.A. Kamakura. Factor analysis with (mixed) observed and latent variables in the exponential family. Psychometrika, 66(4):515-530, 2001. (Pubitemid 33570085)
    • (2001) Psychometrika , vol.66 , Issue.4 , pp. 515-530
    • Wedel, M.1    Kamakura, W.A.2
  • 34
    • 84897525607 scopus 로고    scopus 로고
    • Boltzmann machines with bounded continuous random variables
    • M. Yasuda and K. Tanaka. Boltzmann machines with bounded continuous random variables. Interdisciplinary Information Sciences, 13(1):25-31, 2007.
    • (2007) Interdisciplinary Information Sciences , vol.13 , Issue.1 , pp. 25-31
    • Yasuda, M.1    Tanaka, K.2
  • 35
    • 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
  • 36
    • 40249113236 scopus 로고    scopus 로고
    • Bayesian analysis of multivariate nominal measures using multivariate multinomial probit models
    • X. Zhang, W.J. Boscardin, and T.R. Belin. Bayesian analysis of multivariate nominal measures using multivariate multinomial probit models. Computational statistics & data analysis, 52(7):3697-3708, 2008.
    • (2008) Computational Statistics & Data Analysis , vol.52 , Issue.7 , pp. 3697-3708
    • Zhang, X.1    Boscardin, W.J.2    Belin, T.R.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.