A New Perspective on an Old Perceptron Algorithm
Abstract
We present a generalization of the Perceptron algorithm. The new algorithm performs a Perceptron-style update whenever the margin of an example is smaller than a predefined value. We derive worst case mistake bounds for our algorithm. As a byproduct we obtain a new mistake bound for the Perceptron algorithm in the inseparable case. We describe a multiclass extension of the algorithm. This extension is used in an experimental evaluation in which we compare the proposed algorithm to the Perceptron algorithm.
Cite
Text
Shalev-Shwartz and Singer. "A New Perspective on an Old Perceptron Algorithm." Annual Conference on Computational Learning Theory, 2005. doi:10.1007/11503415_18Markdown
[Shalev-Shwartz and Singer. "A New Perspective on an Old Perceptron Algorithm." Annual Conference on Computational Learning Theory, 2005.](https://mlanthology.org/colt/2005/shalevshwartz2005colt-new/) doi:10.1007/11503415_18BibTeX
@inproceedings{shalevshwartz2005colt-new,
title = {{A New Perspective on an Old Perceptron Algorithm}},
author = {Shalev-Shwartz, Shai and Singer, Yoram},
booktitle = {Annual Conference on Computational Learning Theory},
year = {2005},
pages = {264-278},
doi = {10.1007/11503415_18},
url = {https://mlanthology.org/colt/2005/shalevshwartz2005colt-new/}
}