Abstract
This paper describes the classification and characteristics of single-classification support vector machine, and the advantage applied it to solve the multi-classification; then, combining the algorithm based on support vector data field description with semi-supervised learning idea, propose a semi-supervised support vector data field description multi-classification learning algorithm. This algorithm determine accept the label and refuse the label by defining the membership of non-target samples; through constructing more super ball on the target sample set and the labeled non-target sample set, realize the multi-classification algorithm based on support vector data field description.
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.
