On Higher-Order Perceptron Algorithms
Abstract
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."
Cite
Text
Gentile et al. "On Higher-Order Perceptron Algorithms." Neural Information Processing Systems, 2007.Markdown
[Gentile et al. "On Higher-Order Perceptron Algorithms." Neural Information Processing Systems, 2007.](https://mlanthology.org/neurips/2007/gentile2007neurips-higherorder/)BibTeX
@inproceedings{gentile2007neurips-higherorder,
title = {{On Higher-Order Perceptron Algorithms}},
author = {Gentile, Claudio and Vitale, Fabio and Brotto, Cristian},
booktitle = {Neural Information Processing Systems},
year = {2007},
pages = {521-528},
url = {https://mlanthology.org/neurips/2007/gentile2007neurips-higherorder/}
}