Gaussian Process Latent Variable Models for Visualisation of High Dimensional Data
暂无分享,去创建一个
[1] R. H. Myers. Classical and modern regression with applications , 1986 .
[2] Bernhard Schölkopf,et al. New Support Vector Algorithms , 2000, Neural Computation.
[3] Stefan Schaal,et al. Local Dimensionality Reduction , 1997, NIPS.
[4] Lennart Ljung,et al. Theory and Practice of Recursive Identification , 1983 .
[5] David W. Scott,et al. Multivariate Density Estimation: Theory, Practice, and Visualization , 1992, Wiley Series in Probability and Statistics.
[6] R. Tibshirani,et al. Generalized additive models for medical research , 1986, Statistical methods in medical research.
[7] Michael I. Jordan,et al. Advances in Neural Information Processing Systems 30 , 1995 .
[8] Michael E. Tipping,et al. Probabilistic Principal Component Analysis , 1999 .
[9] Christopher G. Atkeson,et al. Model-Based Control of a Robot Manipulator , 1988 .
[10] Christian Lebiere,et al. The Cascade-Correlation Learning Architecture , 1989, NIPS.
[11] Christopher M. Bishop,et al. GTM: The Generative Topographic Mapping , 1998, Neural Computation.
[12] John C. Platt. A Resource-Allocating Network for Function Interpolation , 1991, Neural Computation.
[13] Carl E. Rasmussen,et al. In Advances in Neural Information Processing Systems , 2011 .
[14] T. Hastie,et al. Local Regression: Automatic Kernel Carpentry , 1993 .
[15] Frank L. Lewis,et al. Aircraft Control and Simulation , 1992 .
[16] Geoffrey E. Hinton,et al. Global Coordination of Local Linear Models , 2001, NIPS.
[17] Bernhard Schölkopf,et al. Kernel Principal Component Analysis , 1997, ICANN.
[18] Brian Everitt,et al. An Introduction to Latent Variable Models , 1984 .
[19] Andrew W. Moore,et al. Locally Weighted Learning , 1997, Artificial Intelligence Review.
[20] Hidemitsu Ogawa,et al. RKHS-based functional analysis for exact incremental learning , 1999, Neurocomputing.
[21] Matthias W. Seeger,et al. Using the Nyström Method to Speed Up Kernel Machines , 2000, NIPS.
[22] William H. Press,et al. Numerical recipes in C , 2002 .
[23] Stefan Schaal,et al. Are internal models of the entire body learnable , 2001 .
[24] Christopher M. Bishop,et al. GTM: A Principled Alternative to the Self-Organizing Map , 1996, NIPS.
[25] Zoubin Ghahramani,et al. Variational Inference for Bayesian Mixtures of Factor Analysers , 1999, NIPS.
[26] J. Friedman,et al. [A Statistical View of Some Chemometrics Regression Tools]: Response , 1993 .
[27] Ian T. Nabney,et al. Netlab: Algorithms for Pattern Recognition , 2002 .
[28] Michael E. Tipping. Sparse Kernel Principal Component Analysis , 2000, NIPS.
[29] Christopher G. Atkeson,et al. Constructive Incremental Learning from Only Local Information , 1998, Neural Computation.
[30] Stefan Schaal,et al. Assessing the Quality of Learned Local Models , 1993, NIPS.
[31] J. Friedman,et al. Projection Pursuit Regression , 1981 .
[32] Jun Nakanishi,et al. Composite adaptive control with locally weighted statistical learning , 2005, Neural Networks.
[33] Ben J. A. Kröse,et al. Supervised Dimension Reduction of Intrinsically Low-Dimensional Data , 2002, Neural Computation.
[34] David B. Dunson,et al. Bayesian Data Analysis , 2010 .
[35] Geoffrey E. Hinton,et al. Stochastic Neighbor Embedding , 2002, NIPS.
[36] J. Tenenbaum,et al. A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.
[37] R. Casey,et al. Advances in Pattern Recognition , 1971 .
[38] Stefan Schaal,et al. Local Adaptive Subspace Regression , 1998, Neural Processing Letters.
[39] Geoffrey E. Hinton,et al. SMEM Algorithm for Mixture Models , 1998, Neural Computation.
[40] Neil D. Lawrence,et al. Fast Sparse Gaussian Process Methods: The Informative Vector Machine , 2002, NIPS.
[41] C. Bishop,et al. Analysis of multiphase flows using dual-energy gamma densitometry and neural networks , 1993 .
[42] Dorothy T. Thayer,et al. EM algorithms for ML factor analysis , 1982 .