A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
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
Newton's Method for Large Bound-Constrained Optimization Problems
SIAM Journal on Optimization
A note on the decomposition methods for support vector regression
Neural Computation
A Modified Finite Newton Method for Fast Solution of Large Scale Linear SVMs
The Journal of Machine Learning Research
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Training a Support Vector Machine in the Primal
Neural Computation
Pegasos: Primal Estimated sub-GrAdient SOlver for SVM
Proceedings of the 24th international conference on Machine learning
A dual coordinate descent method for large-scale linear SVM
Proceedings of the 25th international conference on Machine learning
Trust Region Newton Method for Logistic Regression
The Journal of Machine Learning Research
A coordinate gradient descent method for nonsmooth separable minimization
Mathematical Programming: Series A and B
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Predicting risk from financial reports with regression
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
The Journal of Machine Learning Research
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Stochastic Methods for l1-regularized Loss Minimization
The Journal of Machine Learning Research
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Support vector regression (SVR) and support vector classification (SVC) are popular learning techniques, but their use with kernels is often time consuming. Recently, linear SVC without kernels has been shown to give competitive accuracy for some applications, but enjoys much faster training/ testing. However, few studies have focused on linear SVR. In this paper, we extend state-of-theart training methods for linear SVC to linear SVR. We show that the extension is straightforward for some methods, but is not trivial for some others. Our experiments demonstrate that for some problems, the proposed linear-SVR training methods can very efficiently produce models that are as good as kernel SVR.