Bayesian Inference in Support Vector Regression

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[1]  Jouko Lampinen,et al.  Bayesian approach for neural networks--review and case studies , 2001, Neural Networks.

[2]  David J. C. MacKay,et al.  A Practical Bayesian Framework for Backpropagation Networks , 1992, Neural Computation.

[3]  David J. C. MacKay,et al.  Bayesian Methods for Backpropagation Networks , 1996 .

[4]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[5]  John Platt,et al.  Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods , 1999 .

[6]  Geoffrey E. Hinton,et al.  Bayesian Learning for Neural Networks , 1995 .

[7]  Alexander J. Smola,et al.  Sparse Greedy Gaussian Process Regression , 2000, NIPS.

[8]  James T. Kwok,et al.  Integrating the evidence framework and the support vector machine , 1999, ESANN.

[9]  Bernhard Schölkopf,et al.  Sparse Greedy Matrix Approximation for Machine Learning , 2000, International Conference on Machine Learning.

[10]  Christopher K. I. Williams Prediction with Gaussian Processes: From Linear Regression to Linear Prediction and Beyond , 1999, Learning in Graphical Models.

[11]  S. Duane,et al.  Hybrid Monte Carlo , 1987 .

[12]  David Mackay,et al.  Probable networks and plausible predictions - a review of practical Bayesian methods for supervised neural networks , 1995 .

[13]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[14]  John C. Platt,et al.  Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .

[15]  James T. Kwok,et al.  Bayesian Support Vector Regression , 2001, AISTATS.

[16]  Carl E. Rasmussen,et al.  In Advances in Neural Information Processing Systems , 2011 .

[17]  E. Parzen STATISTICAL INFERENCE ON TIME SERIES BY RKHS METHODS. , 1970 .

[18]  S. Sathiya Keerthi,et al.  Improvements to the SMO algorithm for SVM regression , 2000, IEEE Trans. Neural Networks Learn. Syst..

[19]  Tomaso Poggio,et al.  A Unified Framework for Regularization Networks and Support Vector Machines , 1999 .

[20]  F. Girosi Models of Noise and Robust Estimates , 1991 .

[21]  Massimiliano Pontil,et al.  On the Noise Model of Support Vector Machines Regression , 2000, ALT.

[22]  Dr. M. G. Worster Methods of Mathematical Physics , 1947, Nature.

[23]  C. Micchelli Interpolation of scattered data: Distance matrices and conditionally positive definite functions , 1986 .

[24]  Wei Chu Extended Support Vector Machines: Theory and Implementation , 2001 .

[25]  Wei Chu,et al.  A Unified Loss Function in Bayesian Framework for Support Vector Regression , 2001, ICML.

[26]  Peter Sollich,et al.  Learning Curves for Gaussian Processes , 1998, NIPS.

[27]  Matthias W. Seeger,et al.  Bayesian Model Selection for Support Vector Machines, Gaussian Processes and Other Kernel Classifiers , 1999, NIPS.

[28]  Jorge Nocedal,et al.  Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization , 1997, TOMS.

[29]  Alexander Gammerman,et al.  Ridge Regression Learning Algorithm in Dual Variables , 1998, ICML.

[30]  G. Wahba Spline models for observational data , 1990 .

[31]  R. Fletcher Practical Methods of Optimization , 1988 .

[32]  Radford M. Neal Bayesian training of backpropagation networks by the hybrid Monte-Carlo method , 1992 .

[33]  Michael E. Tipping The Relevance Vector Machine , 1999, NIPS.

[34]  Michael E. Tipping Sparse Kernel Principal Component Analysis , 2000, NIPS.

[35]  Wray L. Buntine,et al.  Bayesian Back-Propagation , 1991, Complex Syst..

[36]  N. Aronszajn Theory of Reproducing Kernels. , 1950 .