In order to discover relevant weights of neural networks, this paper proposes a novel method to learn a distinct squared penalty factor for each weight as a minimization problem over the cross-validation error. Experiments showed that the proposed method works well in discovering a polynomial-type law even from data containing irrelevant variables and a small amount of noise.
['Kazumi Saito', 'Ryohei Nakano']
Discovery of Relevant Weights by Minimizing Cross-Validation Error