Classifying isogenous fields

Abstract

Classifiers that utilize style context in co-occurring patterns increase recognition accuracy. When patterns occur as long isogenous fields, this gain should increase unless negated by parameter estimation errors that increase with field length. We show that our method achieves higher accuracy with longer input fields because it can be trained accurately We also present some ongoing work on simple heuristics to reduce computational complexity of the scheme.