Abstract
The aim of this expository paper is to explain to graduate students and beginning researchers in the field of mathematics, statistics and engineering the fundamental concept of sparse machine learning in Banach spaces. In particular, we use binary classification as an example to explain the essence of learning in a reproducing kernel Hilbert space and sparse learning in a reproducing kernel Banach space (RKBS). We then utilize the Banach space ℓ1(N) to illustrate the basic concepts of the RKBS in an elementary yet rigorous fashion. This paper reviews existing results in the author's perspectives to reflect the state of the art of the field of sparse learning, and includes new theoretical observations on the RKBS. Several open problems critical to the theory of the RKBS are also discussed at the end of this paper.
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.
