Mean field method for the support vector machine regression
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[1] Peter Sollich. Approximate learning curves for Gaussian processes , 1999 .
[2] Federico Girosi,et al. An improved training algorithm for support vector machines , 1997, Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop.
[3] Bernhard Schölkopf,et al. Learning with kernels , 2001 .
[4] Junbin Gao,et al. A Probabilistic Framework for SVM Regression and Error Bar Estimation , 2002, Machine Learning.
[5] Christopher K. I. Williams. Regression with Gaussian processes , 1997 .
[6] Massimiliano Pontil,et al. On the Noise Model of Support Vector Machines Regression , 2000, ALT.
[7] G. Wahba. Support vector machines, reproducing kernel Hilbert spaces, and randomized GACV , 1999 .
[8] Tomaso Poggio,et al. A Unified Framework for Regularization Networks and Support Vector Machines , 1999 .
[9] Michael I. Jordan,et al. Bayesian parameter estimation via variational methods , 2000, Stat. Comput..
[10] Michael I. Jordan. Learning in Graphical Models , 1999, NATO ASI Series.
[11] David Barber,et al. Bayesian Classification With Gaussian Processes , 1998, IEEE Trans. Pattern Anal. Mach. Intell..
[12] David Barber,et al. Gaussian Processes for Bayesian Classification via Hybrid Monte Carlo , 1996, NIPS.
[13] Peter Sollich. Probabilistic interpretations and Bayesian methods for support vector machines , 1999 .
[14] Christopher K. I. Williams. Computing with Infinite Networks , 1996, NIPS.
[15] R. Vanderbei. LOQO:an interior point code for quadratic programming , 1999 .
[16] R. Palmer,et al. Solution of 'Solvable model of a spin glass' , 1977 .
[17] Alexander J. Smola,et al. Learning with kernels , 1998 .
[18] David Maxwell Chickering,et al. Learning Bayesian Networks: The Combination of Knowledge and Statistical Data , 1994, Machine Learning.
[19] Ole Winther,et al. Gaussian Processes for Classification: Mean-Field Algorithms , 2000, Neural Computation.
[20] L. Galway. Spline Models for Observational Data , 1991 .
[21] John C. Platt,et al. Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .
[23] Marti A. Hearst. Trends & Controversies: Support Vector Machines , 1998, IEEE Intell. Syst..
[24] Michael I. Jordan,et al. Variational Probabilistic Inference and the QMR-DT Network , 2011, J. Artif. Intell. Res..
[25] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[26] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[27] Christopher K. I. Williams. Prediction with Gaussian Processes: From Linear Regression to Linear Prediction and Beyond , 1999, Learning in Graphical Models.
[28] S. Gunn. Support Vector Machines for Classification and Regression , 1998 .
[29] Radford M. Neal. Monte Carlo Implementation of Gaussian Process Models for Bayesian Regression and Classification , 1997, physics/9701026.
[30] Michael I. Jordan,et al. Variational probabilistic inference and the QMR-DT database , 1998 .
[31] Thorsten Joachims,et al. Making large scale SVM learning practical , 1998 .
[32] Carl E. Rasmussen,et al. In Advances in Neural Information Processing Systems , 2011 .