In this paper, we propose a method for support vector machine classification using indefinite kernels. Instead of directly minimizing or stabilizing a nonconvex loss function, our...
Based on studies and experiments on the loss term of SVMs, we argue that 1-norm measurement is better than 2-norm measurement for outlier resistance. Thus, we modify the previous 2...
In this study, a support vector machine (SVM) classifies real world data of cytogenetic signals measured from fluorescence in-situ hybridization (FISH) images in order to diagnose...
Contact map prediction is of great interest for its application in fold recognition and protein 3D structure determination. In this paper we present a contact-map prediction algor...
In recent years, Support Vector Machines (SVMs) were successfully applied to a wide range of applications. Their good performance is achieved by an implicit non-linear transformat...
David Martens, Bart Baesens, Tony Van Gestel, Jan ...