Sparse least square twin support vector machine with adaptive norm

摘要

By promoting the parallel hyperplanes to non-parallel ones in SVM, twin support vector machines (TWSVM) have attracted more attention. There are many modifications of them. However, most of the modifications minimize the loss function subject to the I 2-norm or I 1-norm penalty. These methods are non-adaptive since their penalty forms are fixed and pre-determined for any types of data. To overcome the above shortcoming, we propose l p norm least square twin support vector machine (l p LSTSVM). Our new model is an adaptive learning procedure with l p -norm (0