A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational 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
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
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
The analysis of decomposition methods for support vector machines
IEEE Transactions on Neural Networks
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
QP Algorithms with Guaranteed Accuracy and Run Time for Support Vector Machines
The Journal of Machine Learning Research
General Polynomial Time Decomposition Algorithms
The Journal of Machine Learning Research
On the complexity of working set selection
Theoretical Computer Science
| Hi-index | 0.00 |
This article gives a comprehensive study on SMO-type (Sequential Minimal Optimization) decomposition methods for training support vector machines. We propose a general and flexible selection of the two-element working set. Main theoretical results include 1) a simple asymptotic convergence proof, 2) a useful explanation of the shrinking and caching techniques, and 3) the linear convergence of this method. This analysis applies to any SMO-type implementation whose selection falls into the proposed framework.