@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",

month = jan,

day = "1",

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",

}