A sparse least squares support vector machine classifier
József Valyon, Gábor Horváth · 2005
Since the early 90's, support vector machines (SVM) are attracting more and more attention due to their applicability to a large number of problems. To overcome the high computational complexity of traditional support vector machines, previously a new technique, the least squares SVM (LS-SVM) has been introduced, but unfortunately a very attractive feature of SVM, namely its sparseness, was lost. LS-SVM simplifies the required computation to solving linear equation set. This equation set embodies all available information about the learning process. By applying modifications to this equation set, we present a least squares version of the least squares support vector machine (LS/sup 2/-SVM). The proposed modification speeds up the calculations and provides better results, but most importantly it concludes a sparse solution.