Similarity-based classifiers require as input only pairwise similarities between examples, and are therefore applicable in a wide range of domains, including domains in which examples do not lie in a common and continuous metric space. In this paper we present a supervised graph-based approach to classification whereby densities are estimated from a graphical representation of the data, and subsequently combined under Bayes rule to arrive at a classification decision. We illustrate the technique on a simple 2-dimensional, and provide results from several real-world domains to demonstrate the feasibility of the approach.
['Andrew Skabar']
Supervised Similarity-Based Classification using a Generative Graph-Based Model