title: Online learning with (multiple) kernels: a review.
abstract: This review examines kernel methods for online learning, in particular, multiclass classification. We examine margin-based approaches, stemming from Rosenblatt's original perceptron algorithm, as well as nonparametric probabilistic approaches that are based on the popular gaussian process framework. We also examine approaches to online learning that use combinations of kernels--online multiple kernel learning. We present empirical validation of a wide range of methods on a protein fold recognition data set, where different biological feature types are available, and two object recognition data sets, Caltech101 and Caltech256, where multiple feature spaces are available in terms of different image feature extraction methods.
authors.affiliation: Department of Statistical Science, Centre for Computational Statistics and Machine Learning, University College London, WC1E 6BT, U.K. t.diethe@cs.ucl.ac.uk
authors.forename: Tom
authors.initials: T
authors.lastname: Diethe
authors.forename: Mark
authors.initials: M
authors.lastname: Girolami