Discovery of Relevant Weights by Minimizing Cross-Validation Error

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.

• Type: Conference
• Year: 2000
• Where: PAKDD
• Authors: Kazumi Saito, Ryohei Nakano
• Keywords: Cross-validation Error, Data Mining, Distinct Squared Penalty, PAKDD 2000, Relevant Weights