Abstract

AbstractWe study the kernel learning problems with ramp loss, a nonconvex but noise‐resistant loss function. In this work, we justify the validity of ramp loss under the classical kernel learning framework. In particular, we show that the generalization bound for empirical ramp risk minimizer is similar to that of convex surrogate losses, which implies kernel learning with such loss function is not only noise‐resistant but, more importantly, statistically consistent. For adapting to real‐time data streams, we introduce PA‐ramp, a heuristic online algorithm based on the passive‐aggressive framework, to solve this learning problem. Empirically, with fewer support vectors, this algorithm achieves robust empirical performances on tested noisy scenarios.

Full Text
Paper version not known

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 call

Disclaimer: 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.