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Poster
On higher-order perceptron algorithms
Claudio Gentile · Fabio Vitale · Cristian Brotto

Tue Dec 04 10:30 AM -- 10:40 AM (PST) @ None #None

A new algorithm for on-line learning linear-threshold functions is proposed which efficiently combines second-order statistics about the data with the "logarithmic behavior" of multiplicative/dual-norm algorithms. An initial theoretical analysis is provided suggesting that our algorithm might be viewed as a standard Perceptron algorithm operating on a transformed sequence of examples with improved margin properties. We also report on experiments carried out on datasets from diverse domains, with the goal of comparing to known Perceptron algorithms (first-order, second-order, additive, multiplicative). Our learning procedure seems to generalize quite well, and converges faster than the corresponding multiplicative baseline algorithms.