Developments of the generative topographic mapping
暂无分享,去创建一个
Christopher M. Bishop | Christopher K. I. Williams | Markus Svensén | Charles M. Bishop | M. Svensén
[1] Ali Mansour,et al. Blind Separation of Sources , 1999 .
[2] Christopher M. Bishop,et al. Mixtures of Probabilistic Principal Component Analyzers , 1999, Neural Computation.
[3] Michael E. Tipping,et al. Probabilistic Principal Component Analysis , 1999 .
[4] Geoffrey E. Hinton,et al. A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants , 1998, Learning in Graphical Models.
[5] Christopher M. Bishop,et al. A Hierarchical Latent Variable Model for Data Visualization , 1998, IEEE Trans. Pattern Anal. Mach. Intell..
[6] Ayoub Ghriss,et al. Mixtures of Probabilistic Principal Component Analysers , 2018 .
[7] Christopher M. Bishop,et al. GTM: The Generative Topographic Mapping , 1998, Neural Computation.
[8] Juha Karhunen,et al. A Maximum Likelihood Approach to Nonlinear Blind Source Separation , 1997, ICANN.
[9] Christopher K. I. Williams,et al. Magnification factors for the GTM algorithm , 1997 .
[10] Geoffrey E. Hinton,et al. GTM through time , 1997 .
[11] Michael E. Tipping,et al. Mixtures of Principal Component Analysers , 1997 .
[12] Akio Utsugi. Hyperparameter Selection for Self-Organizing Maps , 1997, Neural Computation.
[13] Christopher K. I. Williams,et al. Magnification factors for the SOM and GTM algorithms , 1997 .
[14] M. Gibbs,et al. Efficient implementation of gaussian processes , 1997 .
[15] AlgorithmsChristopher,et al. Magni cation Factors for the SOMand GTM , 1997 .
[16] C. J.,et al. Maximum Likelihood and Covariant Algorithms for Independent Component Analysis , 1996 .
[17] Barak A. Pearlmutter,et al. A Context-Sensitive Generalization of ICA , 1996 .
[18] Carl E. Rasmussen,et al. In Advances in Neural Information Processing Systems , 2011 .
[19] Vladimir Cherkassky,et al. Self-Organization as an Iterative Kernel Smoothing Process , 1995, Neural Computation.
[20] Terrence J. Sejnowski,et al. An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.
[21] D. Mackay,et al. Bayesian neural networks and density networks , 1995 .
[22] Pierre Comon,et al. Independent component analysis, A new concept? , 1994, Signal Process..
[23] Geoffrey E. Hinton,et al. Hand-printed digit recognition using deformable models , 1994 .
[24] Heekuck Oh,et al. Neural Networks for Pattern Recognition , 1993, Adv. Comput..
[25] Mike Rees,et al. 5. Statistics for Spatial Data , 1993 .
[26] Radford M. Neal. A new view of the EM algorithm that justifies incremental and other variants , 1993 .
[27] R. Tibshirani. Principal curves revisited , 1992 .
[28] David J. C. MacKay,et al. A Practical Bayesian Framework for Backpropagation Networks , 1992, Neural Computation.
[29] David J. C. MacKay,et al. Bayesian Interpolation , 1992, Neural Computation.
[30] Tariq Samad,et al. Self–organization with partial data , 1992 .
[31] Christian Jutten,et al. Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture , 1991, Signal Process..
[32] P. McCullagh,et al. Generalized Linear Models, 2nd Edn. , 1990 .
[33] F. Girosi,et al. Networks for approximation and learning , 1990, Proc. IEEE.
[34] R. T. Cox. Probability, frequency and reasonable expectation , 1990 .
[35] Richard Szeliski,et al. An Analysis of the Elastic Net Approach to the Traveling Salesman Problem , 1989, Neural Computation.
[36] J. Berger. Statistical Decision Theory and Bayesian Analysis , 1988 .
[37] Richard Durbin,et al. An analogue approach to the travelling salesman problem using an elastic net method , 1987, Nature.
[38] A. Yaglom. Basic Properties of Stationary Random Functions , 1987 .
[39] A. Yaglom. Correlation Theory of Stationary and Related Random Functions I: Basic Results , 1987 .
[40] James O. Berger,et al. Statistical Decision Theory and Bayesian Analysis, Second Edition , 1985 .
[41] Brian Everitt,et al. An Introduction to Latent Variable Models , 1984 .
[42] T. Kohonen. Self-organized formation of topographically correct feature maps , 1982 .
[43] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[44] Donald B. Rubin,et al. Max-imum Likelihood from Incomplete Data , 1972 .
[45] D. J. Farlie,et al. Prediction and Regulation by Linear Least-Square Methods , 1964 .