Pattern recognition at different scales: A statistical perspective

Matteo Colangeli, Francesco Rugiano and Eros Pasero

Chaos, Solitons & Fractals, 2014, vol. 64, issue C, 48-66

Abstract: 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.

Date: 2014
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:64:y:2014:i:c:p:48-66

DOI: 10.1016/j.chaos.2013.10.006

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