@inproceedings{b6705e8c4b2e46fcb7c9d9a56198a28b,
title = "A new perspective on an old perceptron algorithm",
abstract = "We present a generalization of the Perceptron algorithm. The new algorithm performs a Perceptron-sty le 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.",
author = "Shai Shalev-Shwartz and Yoram Singer",
year = "2005",
doi = "10.1007/11503415_18",
language = "English (US)",
isbn = "3540265562",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "264--278",
booktitle = "Learning Theory - 18th Annual Conference on Learning Theory, COLT 2005, Proceedings",
address = "Germany",
note = "18th Annual Conference on Learning Theory, COLT 2005 - Learning Theory ; Conference date: 27-06-2005 Through 30-06-2005",
}