正在加载图片...
References [1]D.J.Bartholomew and M.Knott.Latent Variable Models and Factor Analysis.Kendall's Library of Statistics,7,second edition,1999. [2]C.M.Bishop.Bayesian PCA.In NIPS 11,1998. [3]J.Chang and D.M.Blei.Relational topic models for document networks.In A/STATS,2009. [4]J.Chang,J.L.Boyd-Graber,and D.M.Blei.Connections between the lines:augmenting social networks with text.In KDD,pages 169-178,2009. [5]W.Chu,V.Sindhwani,Z.Ghahramani,and S.S.Keerthi.Relational learning with Gaussian processes.In NIPS 19.2007. [6]F.Chung.Spectral Graph Theory.Number 92 in Regional Conference Series in Mathematics. American Mathematical Society,1997. [7]D.A.Cohn and T.Hofmann.The missing link-a probabilistic model of document content and hypertext connectivity.In NIPS /3.2000. [8]M.Craven,D.DiPasquo,D.Freitag,A.McCallum,T.M.Mitchell,K.Nigam,and S.Slattery. Learning to extract symbolic knowledge from the world wide web.In 4441/144/,pages 509- 516.1998 [9]A.Dempster,N.Laird,and D.Rubin.Maximum likelihood from incomplete data via the EM algorithm.Journal of the Royal Statistical Society,39(1):1-38,1977. [10]L.Getoor and B.Taskar.Introduction to Statistical Relational Learning.The MIT Press,2007. [11]A.K.Gupta and D.K.Nagar.Matrix Variate Distributions.Chapman Hall/CRC,2000. [12]H.Howard.Analysis of a complex of statistical variables into principal components.Journal of Educational Psychology,27:417-441,1933. [13]I.T.Jolliffe.Principal Component Analysis.Springer,second edition,2002 [14]W.-J.Li and D.-Y.Yeung.Relation regularized matrix factorization.In //CA/2009 [15]W.-J.Li,Z.Zhang,and D.-Y.Yeung.Latent Wishart processes for relational kernel learning. In A/STATS,pages 336-343,2009. [16]A.McCallum,K.Nigam,J.Rennie,and K.Seymore.Automating the construction of internet portals with machine learning.Information Retrieval,3(2):127-163,2000. [17]R.Nallapati,A.Ahmed,E.P.Xing,and W.W.Cohen.Joint latent topic models for text and citations.In KDD,pages 542-550,2008. [18]C.E.Rasmussen and C.K.I.Williams.Gaussian Processes for Machine Learning.The MIT Press,2006 [19]R.Silva,W.Chu,and Z.Ghahramani.Hidden common cause relations in relational learning. In NIPS20.2008. [20]B.Taskar,P.Abbeel,and D.Koller.Discriminative probabilistic models for relational data.In UAL,pages485492,2002. [21]M.E.Tipping and C.M.Bishop.Probabilistic principal component analysis.Journal Of The Roval Statistical Society Series B.61(3):611-622.1999. [22]J.-P.Vert.Reconstruction of biological networks by supervised machine learning approaches. In Elements of Computational Systems Biology,2009. [23]T.Yang,R.Jin,Y.Chi,and S.Zhu.A Bayesian framework for community detection integrating content and link.In UA/,2009. [24]T.Yang,R.Jin,Y.Chi,and S.Zhu.Combining link and content for community detection:a discriminative approach.In KDD,pages 927-936,2009. [25]D.Zhou,B.Scholkopf,and T.Hofmann.Semi-supervised learning on directed graphs.In WIPS17.2004. [26]S.Zhu,K.Yu,Y.Chi,and Y.Gong.Combining content and link for classification using matrix factorization.In S/G/R.2007. 9References [1] D. J. Bartholomew and M. Knott. Latent Variable Models and Factor Analysis. Kendall’s Library of Statistics,7, second edition, 1999. [2] C. M. Bishop. Bayesian PCA. In NIPS 11, 1998. [3] J. Chang and D. M. Blei. Relational topic models for document networks. In AISTATS, 2009. [4] J. Chang, J. L. Boyd-Graber, and D. M. Blei. Connections between the lines: augmenting social networks with text. In KDD, pages 169–178, 2009. [5] W. Chu, V. Sindhwani, Z. Ghahramani, and S. S. Keerthi. Relational learning with Gaussian processes. In NIPS 19, 2007. [6] F. Chung. Spectral Graph Theory. Number 92 in Regional Conference Series in Mathematics. American Mathematical Society, 1997. [7] D. A. Cohn and T. Hofmann. The missing link - a probabilistic model of document content and hypertext connectivity. In NIPS 13, 2000. [8] M. Craven, D. DiPasquo, D. Freitag, A. McCallum, T. M. Mitchell, K. Nigam, and S. Slattery. Learning to extract symbolic knowledge from the world wide web. In AAAI/IAAI, pages 509– 516, 1998. [9] A. Dempster, N. Laird, and D. Rubin. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, 39(1):1–38, 1977. [10] L. Getoor and B. Taskar. Introduction to Statistical Relational Learning. The MIT Press, 2007. [11] A. K. Gupta and D. K. Nagar. Matrix Variate Distributions. Chapman & Hall/CRC, 2000. [12] H. Howard. Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology, 27:417–441, 1933. [13] I. T. Jolliffe. Principal Component Analysis. Springer, second edition, 2002. [14] W.-J. Li and D.-Y. Yeung. Relation regularized matrix factorization. In IJCAI, 2009. [15] W.-J. Li, Z. Zhang, and D.-Y. Yeung. Latent Wishart processes for relational kernel learning. In AISTATS, pages 336–343, 2009. [16] A. McCallum, K. Nigam, J. Rennie, and K. Seymore. Automating the construction of internet portals with machine learning. Information Retrieval, 3(2):127–163, 2000. [17] R. Nallapati, A. Ahmed, E. P. Xing, and W. W. Cohen. Joint latent topic models for text and citations. In KDD, pages 542–550, 2008. [18] C. E. Rasmussen and C. K. I. Williams. Gaussian Processes for Machine Learning. The MIT Press, 2006. [19] R. Silva, W. Chu, and Z. Ghahramani. Hidden common cause relations in relational learning. In NIPS 20. 2008. [20] B. Taskar, P. Abbeel, and D. Koller. Discriminative probabilistic models for relational data. In UAI, pages 485–492, 2002. [21] M. E. Tipping and C. M. Bishop. Probabilistic principal component analysis. Journal Of The Royal Statistical Society Series B, 61(3):611–622, 1999. [22] J.-P. Vert. Reconstruction of biological networks by supervised machine learning approaches. In Elements of Computational Systems Biology, 2009. [23] T. Yang, R. Jin, Y. Chi, and S. Zhu. A Bayesian framework for community detection integrating content and link. In UAI, 2009. [24] T. Yang, R. Jin, Y. Chi, and S. Zhu. Combining link and content for community detection: a discriminative approach. In KDD, pages 927–936, 2009. [25] D. Zhou, B. Scholkopf, and T. Hofmann. Semi-supervised learning on directed graphs. In ¨ NIPS 17, 2004. [26] S. Zhu, K. Yu, Y. Chi, and Y. Gong. Combining content and link for classification using matrix factorization. In SIGIR, 2007. 9
<<向上翻页
©2008-现在 cucdc.com 高等教育资讯网 版权所有