On higher-order perceptron algorithms
Claudio Gentile, Cristian Brotto and Fabio Vitale
In: NIPS 2007, 3-6 Dec 2007, Vancouver, Canada.
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.