Abstract
Learning with indefinite kernels attracted considerable attention in recent years due to their success in various learning scenarios. In this paper we study the asymptotic properties of the regularization kernel networks where the kernels are assumed to be indefinite, without the usual restrictions of symmetry and positive semi-definiteness as in the traditional study of kernel methods. The kernels are characterized in terms of the singular value decomposition of the corresponding kernel integrals. Two reproducing kernel Hilbert spaces are induced to characterize the approximation ability. Capacity independent error bounds are proved. Fast convergence rates are obtained both in reproducing kernel Hilbert spaces and in L2 sense.
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.
