Generalization Error of Combined Classifiers

Generalization Error of Combined Classifiers

We derive an upper bound on the generalization error of classi ers which can be represented as thresholded convex combinations of thresholded convex combinations of functions. Such classi ers include single hidden-layer threshold networks and voted combinations of decision trees (such as those produced by boosting algorithms). The derived bound depends on the proportion of training examples wit...

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Some New Bounds on the Generalization Error of Combined Classifiers

In this paper we develop the method of bounding the generalization error of a classifier in terms of its margin distribution which was introduced in the recent papers of Bartlett and Schapire, Freund, Bartlett and Lee. The theory of Gaussian and empirical processes allow us to prove the margin type inequalities for the most general functional classes, the complexity of the class being measured ...

Bounding the Generalization Error of Neural Networks and Combined Classifiers

Recently, several authors developed a new approach to bounding the generalization error of complex classi-ers (of large or even innnite VC-dimension) obtained by combining simpler classiiers. The new bounds are in terms of the distributions of the margin of combined classiiers and they provide some theoretical explanation of generalization performance of large neu-We obtained new probabilistic ...

Generalization Error of Invariant Classifiers

This paper studies the generalization error of invariant classifiers. In particular, we consider the common scenario where the classification task is invariant to certain transformations of the input, and that the classifier is constructed (or learned) to be invariant to these transformations. Our approach relies on factoring the input space into a product of a base space and a set of transform...