Nonextensive statistical mechanics for hybrid learning of neural networks

Aristoklis D. Anastasiadis and George D. Magoulas

Physica A: Statistical Mechanics and its Applications, 2004, vol. 344, issue 3, 372-382

Abstract: This paper introduces a new hybrid approach for learning systems that builds on the theory of nonextensive statistical mechanics. The proposed learning scheme uses only the sign of the gradient, and combines adaptive stepsize local searches with global search steps that make use of an annealing schedule inspired from nonextensive statistics, as proposed by Tsallis. The performance of the hybrid approach is empirically investigated through simulation in benchmark problems from the UCI Repository of Machine Learning Databases. Preliminary results provide evidence that the synergy of techniques from nonextensive statistics provide neural learning schemes with significant benefits in terms of learning speed and convergence success.

Keywords: Artificial neural networks; Generalized simulated annealing; Global search; Gradient descent; Nonextensive statistics; Pattern classification; Resilient propagation; Supervised learning (search for similar items in EconPapers)
Date: 2004
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:344:y:2004:i:3:p:372-382

DOI: 10.1016/j.physa.2004.06.005

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Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

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