A New Approximate Maximal Margin Classification Algorithm (2001)
| Venue: | JOURNAL OF MACHINE LEARNING RESEARCH |
| Citations: | 60 - 5 self |
BibTeX
@MISC{Gentile01anew,
author = {Claudio Gentile},
title = { A New Approximate Maximal Margin Classification Algorithm},
year = {2001}
}
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Abstract
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 alma p (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. alma p 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 alma 2 (i.e., alma p 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 alma 2 are superior to those achieved by the Perceptron algorithm and ROMMA, but slightly inferior to SVM's. On the other hand, alma 2 is quite faster and easier to implement than standard SVM training algorithms. When learning sparse target vectors, alma p with p > 2 largely outperforms Perceptron-like algorithms, such as alma 2 .
