We present a simple general scheme for improving margins that is inspired on well known margin theory principles. The scheme is based on a sample re-weighting strategy. The very basic idea is in fact to add to the training set new replicas of samples which are not classified with a sufficient margin. As a study case, we present a new algorithm, namely TVQ, which is an instance of the proposed scheme and involves a tangent distance based 1-NN classifier implementing a sort of quantization of the tangent distance prototypes. The tangent distance models created in this way have shown a significant improvement in generalization power with respect to standard tangent models. Moreover, the obtained models were able to outperform other state of the art algorithms, such as SVM, in an OCR task.

A Re-weighting Strategy for Improving Margins

AIOLLI, FABIO;SPERDUTI, ALESSANDRO
2002

Abstract

We present a simple general scheme for improving margins that is inspired on well known margin theory principles. The scheme is based on a sample re-weighting strategy. The very basic idea is in fact to add to the training set new replicas of samples which are not classified with a sufficient margin. As a study case, we present a new algorithm, namely TVQ, which is an instance of the proposed scheme and involves a tangent distance based 1-NN classifier implementing a sort of quantization of the tangent distance prototypes. The tangent distance models created in this way have shown a significant improvement in generalization power with respect to standard tangent models. Moreover, the obtained models were able to outperform other state of the art algorithms, such as SVM, in an OCR task.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11577/2471620
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