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Nonextensive statistical mechanics for hybrid learning of neural networks

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  • Anastasiadis, Aristoklis D.
  • Magoulas, George D.

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.

Suggested Citation

  • Anastasiadis, Aristoklis D. & Magoulas, George D., 2004. "Nonextensive statistical mechanics for hybrid learning of neural networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 344(3), pages 372-382.
    • Handle: RePEc:eee:phsmap:v:344:y:2004:i:3:p:372-382
    DOI: 10.1016/j.physa.2004.06.005
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    References listed on IDEAS

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    1. Tsallis, Constantino & Stariolo, Daniel A., 1996. "Generalized simulated annealing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 233(1), pages 395-406.
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    1. Dukkipati, Ambedkar & Bhatnagar, Shalabh & Murty, M. Narasimha, 2007. "On measure-theoretic aspects of nonextensive entropy functionals and corresponding maximum entropy prescriptions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 384(2), pages 758-774.
    2. Dukkipati, Ambedkar & Murty, M. Narasimha & Bhatnagar, Shalabh, 2006. "Nonextensive triangle equality and other properties of Tsallis relative-entropy minimization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 361(1), pages 124-138.
    3. Aristoklis D. Anastasiadis & Marcelo P. Albuquerque & Marcio P. Albuquerque & Diogo B. Mussi, 2010. "Tsallis q-exponential describes the distribution of scientific citations—a new characterization of the impact," Scientometrics, Springer;Akadémiai Kiadó, vol. 83(1), pages 205-218, April.

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