Abstract-This paper points out an important source of inefficiency in Smola and Schölkopf's sequential minimal optimization (SMO) algorithm for support vector machine (SVM) regression 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 modifications of SMO for regression. These modified algorithms perform significantly faster than the original SMO on the datasets tried. Index Terms-Quadratic programming, regression, sequential minimal optimization (SMO) algorithm, support vector machine (SVM).
is an improved algorithm for addressing regression shortcomings using support vector machines REF .
Improvements to the SMO algorithm for SVM regression
IEEE Trans. Neural Netw
Medicine Computer Science