Abstract
This paper is about two generalizations of the mistake bound model to online multiclass classification. In the standard model, the learner receives the correct classification at the end of each round, and in the bandit model, the learner only finds out whether its prediction was correct or not. For a set F of multiclass classifiers, let optstd(F) and optbandit(F) be the optimal bounds for learning F according to these two models. We show that anoptbandit(F)≤(1+o(1))(|Y|ln|Y|)optstd(F) bound is the best possible up to the leading constant, closing a Θ(log|Y|) factor gap.
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.
