Abstract
In this paper, a new method to determine the penalty coefficients for different samples for the support vector machine (SVM) algorithm was proposed. Sequential minimal optimization (SMO) was then used to solve the SVM problem. Simulation results from applying the proposed method to binary classification problems show that the generalization error of the proposed method was smaller than standard SVM algorithm in the cases that the sizes of binary sample training sets (1) were selected in proportion; (2) were the same; (3) were quite different.
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.
