Multitask centroid twin support vector machines

Twin support vector machines are a recently proposed learning method for binary classification. They learn two hyperplanes rather than one as in conventional support vector machines and often bring performance improvements. However, an inherent shortage of twin support vector machines is that the resultant hyperplanes are very sensitive to outliers in data. In this paper, we propose centroid twin support vector machines to overcome this disadvantage. Furthermore, inspired by the recent success of multitask learning which trains multiple related tasks simultaneously, we also extend them to the multitask learning scenario and propose multitask centroid twin support vector machines. Experimental results demonstrate that our proposed methods are effective.