A New Approximate Maximal Margin Classification Algorithm

A new incremental learning algorithm is described which approximates the maximal margin hyperplane w.r.t. norm p 2 for a set of linearly separable data. Our algorithm, called almap (Approximate Large Margin algorithm w.r.t. norm p), takes O (p-1) 2 2 corrections to separate the data with p-norm margin larger than (1 - ) , where is the (normalized) p-norm margin of the data. almap avoids quadratic (or higher-order) programming methods. It is very easy to implement and is as fast as on-line algorithms, such as Rosenblatt's Perceptron algorithm. We performed extensive experiments on both real-world and artificial datasets. We compared alma2 (i.e., almap with p = 2) to standard Support vector Machines (SVM) and to two incremental algorithms: the Perceptron algorithm and Li and Long's ROMMA. The accuracy levels achieved by alma2 are superior to those achieved by the Perceptron algorithm and ROMMA, but slightly inferior to SVM's. On the other hand, alma2 is quite faster and...

• Type: Conference
• Year: 2000
• Where: NIPS
• Authors: Claudio Gentile
• Keywords: Margin Algorithm W.r.t, NIPS 2000, NIPS 2007, Perceptron Algorithm, Rosenblatt's Perceptron Algorithm