Chapter

Learning Theory

Volume 3559 of the series Lecture Notes in Computer Science pp 264-278

A New Perspective on an Old Perceptron Algorithm

  • Shai Shalev-ShwartzAffiliated withSchool of Computer Sci. & Eng., The Hebrew UniversityGoogle Inc.
  • , Yoram SingerAffiliated withSchool of Computer Sci. & Eng., The Hebrew UniversityGoogle Inc.

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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.