Abstract
Despite support vector machines' (SVM) robustness and optimality, SVM do not scale well computationally. Suffering from slow training convergence on large datasets, SVM online testing time can be suboptimal because SVM write the classifier hyper-plane model as a sum of support vectors that could total as much as half the datasets. Motivated to speed up SVM real time testing by reducing the number of SV, we introduce in this paper a novel local mixture based SVM (LMSVM) approach that exploits the increased separability provided by the kernel trick, while introducing a onetime computational expense. LMSVM applies kernel k-means clustering to the data in kernel space before pruning unwanted clusters based on a mixture measure for label heterogeneity. LMSVM's computational complexity and classification accuracy on four databases from UCI show promising results and motivate follow on research.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.