Efficient Sequential Minimal Optimisation of Support Vector Classifiers
This paper describes a simple modification to the sequential minimal optimisation (SMO) training algorithm for support vector machine (SVM) classifiers, reducing training time at the expense of a small increase in memory used proportional to the number of training patterns. Results obtained on real-world pattern recognition tasks indicate that the proposed modification can more than halve the average training time.
KeywordsSupport Vector Machine Support Vector Lagrange Multiplier Training Time Training Pattern
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