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Application of the method of maximum entropy in the mean to classification problems

Author

Listed:
  • Gzyl, Henryk
  • ter Horst, Enrique
  • Molina, German

Abstract

In this note we propose an application of the method of maximum entropy in the mean to solve a class of inverse problems comprising classification problems and feasibility problems appearing in optimization. Such problems may be thought of as linear inverse problems with convex constraints imposed on the solution as well as on the data. The method of maximum entropy in the mean proves to be a very useful tool to deal with this type of problems.

Suggested Citation

  • Gzyl, Henryk & ter Horst, Enrique & Molina, German, 2015. "Application of the method of maximum entropy in the mean to classification problems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 437(C), pages 101-108.
    • Handle: RePEc:eee:phsmap:v:437:y:2015:i:c:p:101-108
    DOI: 10.1016/j.physa.2015.05.105
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    References listed on IDEAS

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    1. Rau, Albrecht & Nadal, Jean-Pierre, 1992. "A model for a multi-class classification machine," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 185(1), pages 428-432.
    2. Neirotti, Juan P. & Saad, David, 2006. "Efficient Bayesian inference for learning in the Ising linear perceptron and signal detection in CDMA," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 365(1), pages 203-210.
    3. Gzyl, Henryk & Mayoral, Silvia, 2010. "A method for determining risk aversion functions from uncertain market prices of risk," Insurance: Mathematics and Economics, Elsevier, vol. 47(1), pages 84-89, August.
    4. Kinzel, Wolfgang, 1993. "Statistical mechanics of generalization: new results for perceptrons," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 200(1), pages 613-618.
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