Yuanhai Shao

Learn More
Support vector machines (SVM) have been promising methods for classification analysis due to their solid mathematical foundations. Clustering-based SVMs are used to solve large samples classification problems and reduce the computational cost. In this paper, we present a density clustering based SVM(DCB-SVM) method to predict polyadenylation signal (PAS) in(More)
This paper presents a new version of support vector machine (SVM) named l 2 − l p SVM (0 < p < 1) which introduces the l p -norm (0 < p < 1) of the normal vector of the decision plane in the standard linear SVM. To solve the nonconvex optimization problem in our model, an efficient algorithm is proposed using the constrained concave–convex procedure.(More)
  • 1