PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Better algorithms for selective sampling
Francesco Orabona and Nicolò Cesa-Bianchi
In: ICML 2011(2011).

Abstract

We study online algorithms for selective sampling that use regularized least squares (RLS) as base classifier. These algorithms typically perform well in practice, and some of them have formal guarantees on their mistake and query rates. We refine and extend these guarantees in various ways, proposing algorithmic variants that exhibit better empirical behavior while enjoying performance guarantees under much more general conditions. We also show a simple way of coupling a generic gradient-based classifier with a specific RLS-based selective sampler, obtaining hybrid algorithms with combined performance guarantees.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:9195
Deposited By:Nicolò Cesa-Bianchi
Deposited On:21 February 2012