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Advances in Natural Computation

Volume 4221 of the series Lecture Notes in Computer Science pp 39-42

Perceptual Learning Inspired Model Selection Method of Neural Networks

  • Ziang LvAffiliated withSchool of Computer and Information Technology, Beijing Jiaotong University
  • , Siwei LuoAffiliated withSchool of Computer and Information Technology, Beijing Jiaotong University
  • , Yunhui LiuAffiliated withSchool of Computer and Information Technology, Beijing Jiaotong University
  • , Yu ZhengAffiliated withSchool of Computer and Information Technology, Beijing Jiaotong University

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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.