Zero-error dissimilarity based classifiers
Machine Learning
2016-01-19 v1 Machine Learning
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.
Cite
@article{arxiv.1601.04451,
title = {Zero-error dissimilarity based classifiers},
author = {Robert P. W. Duin and Elzbieta Pekalska},
journal= {arXiv preprint arXiv:1601.04451},
year = {2016}
}
Comments
5 pages. Paper originally written in 2003. Although it may proof an obvious fact, it is significant for understanding the essential conditions it is based on