A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Making large-scale support vector machine learning practical
Advances in kernel methods
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Semiparametric support vector and linear programming machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Convergence of a Generalized SMO Algorithm for SVM Classifier Design
Machine Learning
New algorithms for singly linearly constrained quadratic programs subject to lower and upper bounds
Mathematical Programming: Series A and B
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Training support vector machines with multiple equality constraints
ECML'05 Proceedings of the 16th European conference on Machine Learning
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We describe a method for solving large-scale semiparametric support vector machines (SVMs) for regression problems. Most of the approaches proposed to date for large-scale SVMs cannot accommodate the multiple equality constraints that appear in semiparametric problems. Our approach uses a decomposition framework, with a primal-dual algorithm to find an approximate saddle point for the min-max formulation of each subproblem. We compare our method with algorithms previously proposed for semiparametric SVMs, and show that it scales well as the number of training examples grows.