[PDF][PDF] Large margin classification using the perceptron algorithm
Y Freund, RE Schapire - Proceedings of the eleventh annual conference …, 1998 - dl.acm.org
Y Freund, RE Schapire
Proceedings of the eleventh annual conference on Computational learning theory, 1998•dl.acm.orgWe introduce and analyze a new algorithm for linear classification whichcombines
Rosenblatt'sperceptron algorithm with Helmbold and Warmuth's leave-one-out method. Like
Vapnik's maximalmargin classifier, our algorithm takes advantage of data that are linearly
separable with large margins. Compared to Vapnik's algorithm, however, ours is much
simpler to implement, and much more efficient in terms of computation time. We also show
that our algorithm can be efficiently used in very high dimensional spaces using kernel …
Rosenblatt'sperceptron algorithm with Helmbold and Warmuth's leave-one-out method. Like
Vapnik's maximalmargin classifier, our algorithm takes advantage of data that are linearly
separable with large margins. Compared to Vapnik's algorithm, however, ours is much
simpler to implement, and much more efficient in terms of computation time. We also show
that our algorithm can be efficiently used in very high dimensional spaces using kernel …
Abstract
We introduce and analyze a new algorithm for linear classification whichcombines Rosenblatt’sperceptron algorithm with Helmbold and Warmuth’s leave-one-out method. Like Vapnik’s maximalmargin classifier, our algorithm takes advantage of data that are linearly separable with large margins. Compared to Vapnik’s algorithm, however, ours is much simpler to implement, and much more efficient in terms of computation time. We also show that our algorithm can be efficiently used in very high dimensional spaces using kernel functions. We performed some experiments using our algorithm, and some variants of it, for classifying images of handwritten digits. The performance of our algorithm is close to, but not as good as, the performance of maximal-margin classifiers on the same problem.
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