Bayesian Inference in Support Vector Regression
暂无分享,去创建一个
Wei Chu | S. Sathiya | Jin B. Ong | Keerthi Chong | S. Sathiya | W. Chu | Keerthi Chong | Jin B. Ong
[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 .