Bayesian Support Vector Machines for Feature Ranking and Selection
暂无分享,去创建一个
Wei Chu | Zoubin Ghahramani | S. Sathiya Keerthi | Chong Jin Ong | Zoubin Ghahramani | S. Keerthi | Wei Chu | C. Ong
[1] Radford M. Neal. Monte Carlo Implementation of Gaussian Process Models for Bayesian Regression and Classification , 1997, physics/9701026.
[2] Geoffrey E. Hinton,et al. Bayesian Learning for Neural Networks , 1995 .
[3] David J. C. MacKay,et al. Bayesian Methods for Backpropagation Networks , 1996 .
[4] Jason Weston,et al. Gene Selection for Cancer Classification using Support Vector Machines , 2002, Machine Learning.
[5] S. Sathiya Keerthi,et al. Improvements to Platt's SMO Algorithm for SVM Classifier Design , 2001, Neural Computation.
[6] Tommi S. Jaakkola,et al. Feature Selection and Dualities in Maximum Entropy Discrimination , 2000, UAI.
[7] Wei Chu,et al. Bayesian support vector regression using a unified loss function , 2004, IEEE Transactions on Neural Networks.
[8] David J. C. MacKay,et al. A Practical Bayesian Framework for Backpropagation Networks , 1992, Neural Computation.
[9] Grace Wahba,et al. Spline Models for Observational Data , 1990 .
[10] Ole Winther,et al. Gaussian Processes for Classification: Mean-Field Algorithms , 2000, Neural Computation.
[11] Wei Chu,et al. Bayesian Trigonometric Support Vector Classifier , 2003, Neural Computation.
[12] Carl E. Rasmussen,et al. In Advances in Neural Information Processing Systems , 2011 .
[13] Bernhard Schölkopf,et al. A Generalized Representer Theorem , 2001, COLT/EuroCOLT.
[14] Ron Kohavi,et al. Irrelevant Features and the Subset Selection Problem , 1994, ICML.
[15] R. Fletcher. Practical Methods of Optimization , 1988 .
[16] Sayan Mukherjee,et al. Feature Selection for SVMs , 2000, NIPS.
[17] Pat Langley,et al. Induction of Selective Bayesian Classifiers , 1994, UAI.
[18] J. Mesirov,et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.
[19] Bernhard E. Boser,et al. A training algorithm for optimal margin classifiers , 1992, COLT '92.
[20] Zoubin Ghahramani,et al. The EM-EP algorithm for Gaussian process classification , 2003 .
[21] G. Wahba. Spline models for observational data , 1990 .
[22] Larry A. Rendell,et al. The Feature Selection Problem: Traditional Methods and a New Algorithm , 1992, AAAI.
[23] Tomaso Poggio,et al. A Unified Framework for Regularization Networks and Support Vector Machines , 1999 .
[24] G. Wahba,et al. Some results on Tchebycheffian spline functions , 1971 .
[25] David Barber,et al. Bayesian Classification With Gaussian Processes , 1998, IEEE Trans. Pattern Anal. Mach. Intell..
[26] Bernhard Schölkopf,et al. A tutorial on support vector regression , 2004, Stat. Comput..
[27] John C. Platt,et al. Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .
[28] Thomas G. Dietterich,et al. Learning with Many Irrelevant Features , 1991, AAAI.
[29] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[30] Christopher J. C. Burges,et al. A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.
[31] Tom Minka,et al. A family of algorithms for approximate Bayesian inference , 2001 .
[32] Ole Winther,et al. Efficient Approaches to Gaussian Process Classification , 1999, NIPS.
[33] Matthias W. Seeger,et al. Bayesian Model Selection for Support Vector Machines, Gaussian Processes and Other Kernel Classifiers , 1999, NIPS.