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.

• Type: Conference
• Year: 2005
• Where: COLT
• Authors: Shai Shalev-Shwartz, Yoram Singer
• Keywords: Case Mistake Bounds, COLT 2005, Mistake Bound, Perceptron Algorithm