Improvements to Platt's SMO Algorithm for SVM Classifier Design

This paper points out an important source of confusion and ineciency in Platt's Sequential Minimal Optimization (SMO) algorithm that is caused by the use of a single threshold value. Using clues from the KKT conditions for the dual problem, two threshold parameters are employed to derive modications of SMO. These modied algorithms perform signicantly faster than the original SMO on all benchmark datasets tried. 1 Introduction In the past few years, there has been a lot of excitement and interest in Support Vector Machines[16, 2] because they have yielded excellent generalization performance on a wide range of problems. Recently, fast iterative algorithms that are also easy to implement have been suggested[9,4,7,3,6]. Platt's Sequential Minimization Algorithm (SMO)[9,11] is an important example. A remarkable feature of SMO is that it is also extremely easy to implement. Comparative testing against other algorithms, done by Platt, have shown that SMO is often much faster and has..

Improvements to the SMO algorithm for SVM regression

10.1109/72.870050IEEE Transactions on Neural Networks1151188-1193ITNN