Support vector machines (SVMs) excel at two-class discriminative learning problems. They often outperform generative classifiers, especially those that use inaccurate generative m...
Effective prediction of defectprone software modules can enable software developers to focus quality assurance activities and allocate effort and resources more efficiently. Supp...
Based on biological data we examine the ability of Support Vector Machines (SVMs) with gaussian kernels to learn and predict the nonlinear dynamics of single biological neurons. We...
Although Support Vector Machines (SVMs) have been successfully applied to solve a large number of classification and regression problems, they suffer from the catastrophic forgetti...
Abstract. Infection by high-risk human papillomaviruses (HPVs) is associated with the development of cervical cancers. Classification of risk types is important to understand the ...