In this paper we borrow concepts from Information Theory and Statistical Mechanics to perform a pattern recognition procedure on a set of X-ray hazelnut images. We identify two relevant statistical scales, whose ratio affects the performance of a machine learning algorithm based on statistical observables, and discuss the dependence of such scales on the image resolution. Finally, by averaging the performance of a Support Vector Machines algorithm over a set of training samples, we numerically verify the predicted onset of an "optimal" scale of resolution, at which the pattern recognition is favoured. © 2013 Elsevier Ltd. All rights reserved.
|Titolo:||Pattern recognition at different scales: A statistical perspective|
|Autori interni:||COLANGELI, MATTEO|
|Data di pubblicazione:||2014|
|Rivista:||CHAOS, SOLITONS AND FRACTALS|
|Appare nelle tipologie:||1.1 Articolo in rivista|