Abstract
From the geometric point of view and by choosing the most informative patterns that have the most possibility to become the support vectors in the training data by using the convex hulls algorithm, a fast training algorithm for SVM is given in this paper. In this training algorithm for SVM, the convex hull vectors are chosen firstly, and the convex hull vectors are used to train the SVM. The characteristics of the convex hulls algorithm are analyzed by experiments with training sets of different size and dimension. Classification experiments results reveal that the given fast training algorithm for SVM has better training performance comparing with the traditional training algorithm for SVM, and has distinct performance improvement when deal with the dataset of low dimension and large size.
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.
