Abstract
In this paper, we proposed a novel nonparallel hyper planes classifier for binary classification, termed as NHC. Though this method can be in fact proved equivalent to an improved twin support vector machine (TWSVM), it has the incomparable advantages than existing TWSVMs. First, the optimization problems in NHC can be solved efficiently by successive over relaxation (SOR) without needing to compute the large inverse matrices before training as TWSVMs usually do, Second, kernel trick can be applied directly to NHC, which is superior to existing TWSVMs. Experimental results on lots of data sets show the efficiency of our method in both computation time and classification accuracy.
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.
