Noise suppression in training examples for improving generalization capability.
For the supervised learning problem, error correcting memorization learning was proposed in order to suppress noise in teacher signals. In this paper, generalization capability of the learning method is discussed. Generalization capability is evaluated based on the projection learning criterion. We give a necessary and sufficient condition for error correcting memorization learning to provide the same level of generalization as projection learning, and suggest how to choose a training set so as to satisfy the obtained condition. Moreover, it is revealed that noise suppression based on the error correcting memorization learning criterion always has a good effect on improving generalization to the level of projection learning.
Nakashima
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A
Corporate Research & Development Center, Toshiba Corporation, Kawasaki, Japan. akiko.nakashima@toshiba.co.jp
Ogawa
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H
D016477:Artifacts; D001185:Artificial Intelligence; D016571:Neural Networks, Computer