Abstract
Radial basis function (RBF) has been widely used in kernel-based approaches. This paper extended RBF kernels to weighted RBF (WRBF) kernels by introducing a weighting matrix A into RBF kernels. A key to success in implementing WRBF kernels is to design different appropriate weighting matrices to implement WRBF kernels. Three weighting matrices are of particular interest, covariance matrix, correlation matrix and within-class scatter matrix. Experimental results via various applications show that classifiers using WRBF kernels provide better performance than that using un-weigheted RBF kernels.
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.
