Abstract
In this paper, we consider the problem of local risk minimization on the basis of empirical data, which is a generalization of the problem of global risk minimization. A new local risk regularization scheme is proposed. The error estimate for the proposed algorithm is obtained by using probabilistic estimates for integral operators. Experiments are presented to illustrate the general theory. Simulation results on several artificial real datasets show that the local risk regularization algorithm has better performance.
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 callMore From: International Journal of Wavelets, Multiresolution and Information Processing
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.
