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Pattern recognition at different scales: A statistical perspective

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  • Colangeli, Matteo
  • Rugiano, Francesco
  • Pasero, Eros

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.

Suggested Citation

  • Colangeli, Matteo & Rugiano, Francesco & Pasero, Eros, 2014. "Pattern recognition at different scales: A statistical perspective," Chaos, Solitons & Fractals, Elsevier, vol. 64(C), pages 48-66.
    • Handle: RePEc:eee:chsofr:v:64:y:2014:i:c:p:48-66
    DOI: 10.1016/j.chaos.2013.10.006
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    1. Anne Condon, 2011. "DNA and the brain," Nature, Nature, vol. 475(7356), pages 304-305, July.
    2. Lulu Qian & Erik Winfree & Jehoshua Bruck, 2011. "Neural network computation with DNA strand displacement cascades," Nature, Nature, vol. 475(7356), pages 368-372, July.
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    Cited by:

    1. Lahmiri, Salim, 2016. "Image characterization by fractal descriptors in variational mode decomposition domain: Application to brain magnetic resonance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 235-243.
    2. Ancillao, Andrea & Galli, Manuela & Rigoldi, Chiara & Albertini, Giorgio, 2014. "Linear correlation between fractal dimension of surface EMG signal from Rectus Femoris and height of vertical jump," Chaos, Solitons & Fractals, Elsevier, vol. 66(C), pages 120-126.

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