On the convergence of the coordinate descent method for convex differentiable minimization
Journal of Optimization Theory and Applications
Making large-scale support vector machine learning practical
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Proximal support vector machine classifiers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
A Simple Decomposition Method for Support Vector Machines
Machine Learning
Polynomial-Time Decomposition Algorithms for Support Vector Machines
Machine Learning
Lagrangian support vector machines
The Journal of Machine Learning Research
Sparseness of support vector machines
The Journal of Machine Learning Research
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
Working Set Selection Using Second Order Information for Training Support Vector Machines
The Journal of Machine Learning Research
Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-supervised, and Unsupervised Learning (Studies in Computational Intelligence)
QP Algorithms with Guaranteed Accuracy and Run Time for Support Vector Machines
The Journal of Machine Learning Research
Maximum-Gain Working Set Selection for SVMs
The Journal of Machine Learning Research
General Polynomial Time Decomposition Algorithms
The Journal of Machine Learning Research
Support Vector Machines
Gaps in support vector optimization
COLT'07 Proceedings of the 20th annual conference on Learning theory
General polynomial time decomposition algorithms
COLT'05 Proceedings of the 18th annual conference on Learning Theory
On the convergence of the decomposition method for support vector machines
IEEE Transactions on Neural Networks
Asymptotic convergence of an SMO algorithm without any assumptions
IEEE Transactions on Neural Networks
A formal analysis of stopping criteria of decomposition methods for support vector machines
IEEE Transactions on Neural Networks
A study on SMO-type decomposition methods for support vector machines
IEEE Transactions on Neural Networks
Density-based similarity measures for content based search
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
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We develop, analyze, and test a training algorithm for support vector machine classifiers without offset. Key features of this algorithm are a new, statistically motivated stopping criterion, new warm start options, and a set of inexpensive working set selection strategies that significantly reduce the number of iterations. For these working set strategies, we establish convergence rates that, not surprisingly, coincide with the best known rates for SVMs with offset. We further conduct various experiments that investigate both the run time behavior and the performed iterations of the new training algorithm. It turns out, that the new algorithm needs significantly less iterations and also runs substantially faster than standard training algorithms for SVMs with offset.