G (2004) A sparse least squares support vector machine classifier

Abstract – 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, recently 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 (LS2–SVM). The proposed modification speeds up the calculations and provides better results, but most importantly it concludes a sparse solution