The nature of statistical learning theory
The nature of statistical learning theory
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
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Duality and Geometry in SVM Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
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
Maximum-Gain Working Set Selection for SVMs
The Journal of Machine Learning Research
A fast iterative nearest point algorithm for support vector machine classifier design
IEEE Transactions on Neural Networks
First and Second Order SMO Algorithms for LS-SVM Classifiers
Neural Processing Letters
Momentum acceleration of least-squares support vector machines
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
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Fast SVM training is an important goal for which many proposals have been given in the literature. In this work we will study from a geometrical point of view the presence, in both the Mitchell-Demyanov-Malozemov (MDM) algorithm and Platt's Sequential Minimal Optimization, of training cycles, that is, the repeated selection of some concrete updating patterns. We shall see how to take advantage of these cycles by partially collapsing them in a single updating vector that gives better minimizing directions. We shall numerically illustrate the resulting procedure, showing that it can lead to substantial savings in the number of iterations and kernel operations for both algorithms.