@article{oai:fukuyama-u.repo.nii.ac.jp:00007935,
 author = {渡辺, 栄治 and 清水, 光},
 issue = {2},
 journal = {福山大学工学部紀要},
 month = {Mar},
 note = {P(論文), This paper studies relationships between multi-layered neural networks and the regression analysis for function approximation problems in the presence of the observation noise. First, we compare NNs with regression models from the viewpoint of both model structures and parameter estimators. Based on the discussions, we propose a learning algorithm for avoidance of overfitting. Next, we propose a method for analysis of the internal representation of NN by using the principal component analysis. Finally, the modeling results of the two modeling methods are concretely shown by several numerical computations.},
 pages = {11--22},
 title = {階層型ニューラルネットワークと回帰分析の関係},
 volume = {18},
 year = {1995}
}