A Stochastic Treatment of Similarity
This study investigates a robust measure of similarity applicable in many domains and across many dimensions of data. Given a distance or discrepancy measure on a domain, the similarity of two values in this domain is defined as the probability that any pair of values from that domain are more different (at a larger distance) than these two values are. We discuss the motivation for this approach, its properties, and the issues that arise from it. © 2010 Springer-Verlag Berlin Heidelberg.
Ralescu, A.; Visa, Sofia; and Popovici, S., "A Stochastic Treatment of Similarity" (2010). Computational Intelligence for Knowledge-Based Systems Design, , 11-18. 10.1007/978-3-642-14049-5_2. Retrieved from https://openworks.wooster.edu/facpub/159