Abstract
Binary tree support vector machine, which combines support vector machine and binary tree, is an effective way for solving multiclass problems. Classification accuracy and decision speed of the classifier relate closely to the structure of the binary tree. To maintain high generalization ability, most separable classes should be separated at upper nodes of a binary tree. And in order to obtain classification results rapidly, levels of the binary tree should be fewer. In this paper, a new binary tree with fewest levels based on clustering method is established. The efficiency of the improved binary tree support vector machine multiclassifier is proved by the results of experiment.
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.
