Abstract
In this paper, we study a more general kernel regression learning with coefficient regularization. A non-iid setting is considered, where the sequence of probability measures for sampling is not identical but the sequence of marginal distributions for sampling converges exponentially fast in the dual of a Holder space; the sampling z i , i ≥ 1 are weakly dependent, which satisfy a strongly mixing condition. Satisfactory capacity independently error bounds and learning rates are derived by the techniques of integral operator for this learning algorithm.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
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.