Admissibility of Memorization Learning with Respect to Projection Learning in the Presence of Noise

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概要

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• In learning of feed-forward neural networks, so-called 'training error' is often minimized. This is, however, not related to the generalization capability which is one of the major goals in the learning. It can be interpreted as a substitute for another learning which considers the generalization capability. Admissibility is a concept to discuss whether a learning can be a substitute for another learning. In this paper, we discuss the case where the learning which minimizes a training error is used as a substitute for the projection learning, which considers the generalization capability, in the presence of noise. Moreover, we give a method for choosing a training set which satisfies the admissibility.

• 社団法人電子情報通信学会の論文
• 1999-02-25

著者

• Hirabayashi A

  Tokyo Inst. Technol. Tokyo Jpn

• Hirabayashi Akira

  Graduate School Of Medicine Yamaguchi University

• OGAWA Hidemitsu

  Graduate School of Information Science and Engineering, Tokyo Institute of Technology

• YAMASHITA Yukihiko

  Faculty of Engineering, Tokyo Institute of Technology

• Ogawa Hidemitsu

  Graduate School Of Information Science And Engineering Tokyo Institute Of Technology

• Yamashita Yukihiko

  Faculty Of Engineering Tokyo Institute Of Technology

• Hirabayashi Akira

  Graduate School Of Medicine Yamaguchi Univ.

関連論文

• Admissibility of Memorization Learning with Respect to Projection Learning in the Presence of Noise
• Partial Projection Filter for Signal Restoration in the Presence of Signal Space Noise(Image Processing and Video Processing)
• Characterization and Implementation of Partial Projection Filter in the Presence of Signal Space Noise(Image Processing and Video Processing)
• Improving Generalization Ability through Active Learning
• Fast Surface Profiling by White-Light Interferometry Using Symmetric Spectral Optical Filter
• Sampling and Reconstruction of Periodic Piecewise Polynomials Using Sinc Kernel
• Consistent Sampling and Signal Reconstruction in Noisy Under-Determined Case

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