Perceptual learning inspired model selection method of neural networks

  • Authors:

  • Ziang Lv;Siwei Luo;Yunhui Liu;Yu Zheng

  • Affiliations:

  • School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China;School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China;School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China;School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China

  • Venue:
  • ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
  • Year:
  • 2006

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Abstract

Perceptual learning is the improvement in performance on a variety of simple sensory tasks. Current neural network models mostly concerned with bottom-up processes, and do not incorporate top-down information. Model selection is the crux of learning. To obtain good model we must make balance between the goodness of fit and the complexity of the model. Inspired by perceptual learning, we studied on the model selection of neuro-manifold, use the geometrical method. We propose that the Gauss-Kronecker curvature of the statistical manifold is the natural measurement of the nonlinearity of the manifold. This approach provides a clear intuitive understanding of the model complexity.