On higher-order perceptron algorithms

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.

• Type: Conference
• Year: 2007
• Where: NIPS
• Authors: Claudio Gentile, Fabio Vitale, Cristian Brotto
• Keywords: Information Technology, Learning Linear-threshold Functions, NIPS 2007, Perceptron Algorithms, Standard Perceptron Algorithm