The nature of statistical learning theory
The nature of statistical learning theory
Advances in kernel methods: support vector learning
Advances in kernel methods: support vector 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
Convergence of a Generalized SMO Algorithm for SVM Classifier Design
Machine Learning
Polynomial-Time Decomposition Algorithms for Support Vector Machines
Machine Learning
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
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
An online core vector machine with adaptive MEB adjustment
Pattern Recognition
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Support vector machines (SVMs) are a new and important tool in data classification. Recently much attention has been devoted to large scale data classifications where decomposition methods for SVMs play an important role. So far, several decomposition algorithms for SVMs have been proposed and applied in practice. The algorithms proposed recently and based on rate certifying pair/set provide very attractive features compared with many other decomposition algorithms. They converge not only with finite termination but also in polynomial time. However, it is difficult to reach a good balance between low computational cost and fast convergence. In this paper, we propose a new simple decomposition algorithm based on a new philosophy on working set selection. It has been proven that the working set selected by the new algorithm is a rate certifying set. Further, compared with the existing algorithms based on rate certifying pair/set, our algorithm provides a very good feature in combination of lower computational complexity and faster convergence.