Deriving distance metrics from generality relations

Authors:
Luc De Raedt;Jan Ramon
Affiliations:
Department of Computer Science, Katholieke Universiteit Leuven, Celestijnenlaan 200A, BE-3001 Heverlee, Belgium;Department of Computer Science, Katholieke Universiteit Leuven, Celestijnenlaan 200A, BE-3001 Heverlee, Belgium
Venue:
Pattern Recognition Letters
Year:
2009

Citing 4
Cited 3

A graph distance metric based on the maximal common subgraph

Pattern Recognition Letters
The String-to-String Correction Problem

Journal of the ACM (JACM)
The Tree-to-Tree Correction Problem

Journal of the ACM (JACM)
A polynomial time computable metric between point sets

Acta Informatica

A framework for reasoning under uncertainty based on non-deterministic distance semantics

International Journal of Approximate Reasoning
Not far away from home: a relational distance-based approach to understanding images of houses

ILP'10 Proceedings of the 20th international conference on Inductive logic programming
A polynomial-time maximum common subgraph algorithm for outerplanar graphs and its application to chemoinformatics

Annals of Mathematics and Artificial Intelligence

Quantified Score

Hi-index	0.10

Visualization

Abstract

Many pattern recognition and machine learning approaches employ a distance metric on patterns, or a generality relation to partially order the patterns. We investigate the relationship amongst them and prove a theorem that shows how a distance metric can be derived from a partial order (and a corresponding size on patterns) under mild conditions. We then discuss the use of the theorem. More specifically, we show how well-known distance metrics for sets, strings, trees and graphs can be derived from their generality relation.