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arXiv · 1601.04451

Zero-error dissimilarity based classifiers

Robert P. W. Duin·Elzbieta Pekalska
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Abstract

We consider general non-Euclidean distance measures between real world objects that need to be classified. It is assumed that objects are represented by distances to other objects only. Conditions for zero-error dissimilarity based classifiers are derived. Additional conditions are given under which the zero-error decision boundary is a continues function of the distances to a finite set of training samples. These conditions affect the objects as well as the distance measure used. It is argued that they can be met in practice.

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Robert P. W. Duin, Elzbieta Pekalska. 2016-01-18. Zero-error dissimilarity based classifiers. https://arxiv.org/abs/1601.04451

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