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
The Kernel-Adatron Algorithm: A Fast and Simple Learning Procedure for Support Vector Machines
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Support Vector Machines: Training and Applications
Support Vector Machines: Training and Applications
SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
Fast SVM Training Algorithm with Decomposition on Very Large Data Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
A tutorial on ν-support vector machines: Research Articles
Applied Stochastic Models in Business and Industry - Statistical Learning
Training ν-Support Vector Classifiers: Theory and Algorithms
Neural Computation
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Successive overrelaxation for support vector machines
IEEE Transactions on Neural Networks
Efficient kernel feature extraction for massive data sets
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Decomposition Algorithms for Training Large-Scale Semiparametric Support Vector Machines
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Automatic aurora images classification algorithm based on separated texture
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Diversified SVM ensembles for large data sets
ECML'06 Proceedings of the 17th European conference on Machine Learning
Nonparallel hyperplane support vector machine for binary classification problems
Information Sciences: an International Journal
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In this paper we present a primal-dual decomposition algorithm for support vector machine training. As with existing methods that use very small working sets (such as Sequential Minimal Optimization (SMO), Successive Over-Relaxation (SOR) or the Kernel Adatron (KA)), our method scales well, is straightforward to implement, and does not require an external QP solver. Unlike SMO, SOR and KA, the method is applicable to a large number of SVM formulations regardless of the number of equality constraints involved. The effectiveness of our algorithm is demonstrated on a more difficult SVM variant in this respect, namely semi-parametric support vector regression.