Mason, Llew; Bartlett, Peter; Baxter, Jon
Recent theoretical results have shown that the generalization performance of thresholded convex combinations of base classifiers is greatly improved if the underlying convex combination has large margins on the training data (i.e., correct examples are classified well away from the decision boundary). Neural network algorithms and AdaBoost have been shown to implicitly maximize margins, thus providing some theoretical justification for their remarkably good generalization performance. In this...[Show more]
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