Maximum Likelihood Model Selection for 1-Norm Soft Margin SVMs with Multiple Parameters
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[1] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[2] N. Cristianini,et al. On Kernel-Target Alignment , 2001, NIPS.
[3] Gavin C. Cawley,et al. Preventing Over-Fitting during Model Selection via Bayesian Regularisation of the Hyper-Parameters , 2007, J. Mach. Learn. Res..
[4] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[5] Ambuj Tewari,et al. Sparseness vs Estimating Conditional Probabilities: Some Asymptotic Results , 2007, J. Mach. Learn. Res..
[6] T. Glasmachers,et al. Gradient-Based Optimization of Kernel-Target Alignment for Sequence Kernels Applied to Bacterial Gene Start Detection , 2007, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[7] S. Sathiya Keerthi,et al. Evaluation of simple performance measures for tuning SVM hyperparameters , 2003, Neurocomputing.
[8] Christian Igel,et al. Empirical evaluation of the improved Rprop learning algorithms , 2003, Neurocomputing.
[9] Chih-Jen Lin,et al. Radius Margin Bounds for Support Vector Machines with the RBF Kernel , 2002, Neural Computation.
[10] Nikolaus Hansen,et al. Completely Derandomized Self-Adaptation in Evolution Strategies , 2001, Evolutionary Computation.
[11] G. Cawley,et al. Efficient approximate leave-one-out cross-validation for kernel logistic regression , 2008, Machine Learning.
[12] H. Damasio,et al. IEEE Transactions on Pattern Analysis and Machine Intelligence: Special Issue on Perceptual Organization in Computer Vision , 1998 .
[13] Christian Igel,et al. Evolutionary tuning of multiple SVM parameters , 2005, ESANN.
[14] Christian Igel,et al. Gradient-Based Adaptation of General Gaussian Kernels , 2005, Neural Computation.
[15] Christian Igel,et al. Uncertainty Handling in Model Selection for Support Vector Machines , 2008, PPSN.
[16] Olivier Chapelle,et al. Support Vector Machines: Induction Principle, Adaptive Tuning and Prior Knowledge , 2002 .
[17] Paul S. Bradley,et al. Feature Selection via Concave Minimization and Support Vector Machines , 1998, ICML.
[18] Tong Zhang. Statistical behavior and consistency of classification methods based on convex risk minimization , 2003 .
[19] Hsuan-Tien Lin,et al. A note on Platt’s probabilistic outputs for support vector machines , 2007, Machine Learning.
[20] John Platt,et al. Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods , 1999 .
[21] Gunnar Rätsch,et al. Soft Margins for AdaBoost , 2001, Machine Learning.
[22] V. Vapnik,et al. Bounds on Error Expectation for Support Vector Machines , 2000, Neural Computation.
[23] Trevor Hastie,et al. The Elements of Statistical Learning , 2001 .
[24] Christian Igel,et al. Maximum-Gain Working Set Selection for SVMs , 2006, J. Mach. Learn. Res..
[25] S. Sathiya Keerthi,et al. An Efficient Method for Gradient-Based Adaptation of Hyperparameters in SVM Models , 2006, NIPS.
[26] Carl Gold,et al. Model selection for support vector machine classification , 2002, Neurocomputing.
[27] Ashutosh Kumar Singh,et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2010 .
[28] Christian Igel,et al. Efficient covariance matrix update for variable metric evolution strategies , 2009, Machine Learning.
[29] David Haussler,et al. Using the Fisher Kernel Method to Detect Remote Protein Homologies , 1999, ISMB.
[30] Sayan Mukherjee,et al. Choosing Multiple Parameters for Support Vector Machines , 2002, Machine Learning.
[31] Matthias W. Seeger,et al. Bayesian Model Selection for Support Vector Machines, Gaussian Processes and Other Kernel Classifiers , 1999, NIPS.
[32] Grace Wahba,et al. Soft and hard classification by reproducing kernel Hilbert space methods , 2002, Proceedings of the National Academy of Sciences of the United States of America.
[33] George Eastman House,et al. Sparse Bayesian Learning and the Relevance Vector Machine , 2001 .
[34] Bernhard E. Boser,et al. A training algorithm for optimal margin classifiers , 1992, COLT '92.
[35] S. Sathiya Keerthi,et al. Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms , 2002, IEEE Trans. Neural Networks.