Recent work shows that Support vector machines (SVMs) can be solved efficiently in the primal. This paper follows this line of research and shows how to build sparse support vector...
In our previous work, we have developed sparse least squares support vector regressors (sparse LS SVRs) trained in the primal form in the reduced empirical feature space. In this p...
Support vector machines (SVMs), though accurate, are not preferred in applications requiring great classification speed, due to the number of support vectors being large. To overc...
S. Sathiya Keerthi, Olivier Chapelle, Dennis DeCos...
Abstract. In this paper we discuss sparse least squares support vector regressors (sparse LS SVRs) defined in the reduced empirical feature space, which is a subspace of mapped tr...
— Support Vector Machine has been well received in machine learning community with its theoretical as well as practical value. However, since its training time complexity is cubi...