Noise Tolerant Variants of the Perceptron Algorithm
Roni Khardon, Gabriel Wachman; 8(8):227−248, 2007.
Abstract
A large number of variants of the Perceptron algorithm have been proposed and partially evaluated in recent work. One type of algorithm aims for noise tolerance by replacing the last hypothesis of the perceptron with another hypothesis or a vote among hypotheses. Another type simply adds a margin term to the perceptron in order to increase robustness and accuracy, as done in support vector machines. A third type borrows further from support vector machines and constrains the update function of the perceptron in ways that mimic soft-margin techniques. The performance of these algorithms, and the potential for combining different techniques, has not been studied in depth. This paper provides such an experimental study and reveals some interesting facts about the algorithms. In particular the perceptron with margin is an effective method for tolerating noise and stabilizing the algorithm. This is surprising since the margin in itself is not designed or used for noise tolerance, and there are no known guarantees for such performance. In most cases, similar performance is obtained by the voted-perceptron which has the advantage that it does not require parameter selection. Techniques using soft margin ideas are run-time intensive and do not give additional performance benefits. The results also highlight the difficulty with automatic parameter selection which is required with some of these variants.
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