Abstract
Support Vector Machine (SVM) is one of the most popular classification algorithms. SVM separates data points into two classes by using the hyper-plane that is maximally distant from the two classes. Since SVM is theoretically based on statistical learning theory and the principle of structural risk minimization, it offers highly accurate classification. However, its training process is computationally expensive. This paper proposes Sahara as an efficient training algorithm for SVM. It identifies data points that have no influence on SVM classification by computing the upper and lower bounds of a parameter that determines the hyper-plane. Our approach can efficiently compute the bounds by using Singular Value Decomposition (SVD) and a sparse data matrix. Theoretically, our approach guarantees to yield the optimal hyper-plane of SVM for any given set of data points. Experiments show that Sahara is significantly faster than previous approaches.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: 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.
