A dynamic logistic multiple classifier system for online classification

Authors:
Amber Tomas
Affiliations:
Department of Statistics, The University of Oxford, UK
Venue:
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Year:
2011

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Abstract

We consider the problem of online classification in nonstationary environments. Specifically, we take a Bayesian approach to sequential parameter estimation of a logistic MCS, and compare this method with other algorithms for nonstationary classification. We comment on several design considerations.