Matrix computations (3rd ed.)
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
A Database for Handwritten Text Recognition Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
An Efficient Implementation of an Active Set Method for SVMs
The Journal of Machine Learning Research
Incremental training of support vector machines
IEEE Transactions on Neural Networks
Convergence improvement of active set training for support vector regressors
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
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Efficiently implemented active set methods have been successfully applied to Support Vector Machine (SVM) training. These active set methods offer higher precision and incremental training at the cost of additional memory requirements when compared to decomposition methods such as Sequential Minimal Optimization (SMO). However, all existing active set methods must deal with singularities occurring within the inner problem solved at each iteration, a problem that leads to more complex implementation and potential inefficiencies. Here, we introduce a revised simplex method, originally introduced by Rusin, adapted for SVM training and show this is an active set method similar to most existing methods with the advantage of maintaining nonsingularity of the inner problem. We compare performance to an existing active set method introduced by Scheinberg and demonstrate an improvement in training times, in some cases. We show our method maintains a slightly simpler implementation and offers advantages in terms of applying iterative methods to alleviate memory concerns. We also show performance of the active set methods when compared to state-of-the-art decomposition implementations such as SVMLight and SMO.