Learning Curves, Model Selection and Complexity of Neural Networks
Abstract
Learning curves show how a neural network is improved as the number of t.raiuing examples increases and how it is related to the network complexity. The present paper clarifies asymptotic properties and their relation of t.wo learning curves, one concerning the predictive loss or generalization loss and the other the training loss. The result gives a natural definition of the complexity of a neural network. Moreover, it provides a new criterion of model selection.
Cite
Text
Murata et al. "Learning Curves, Model Selection and Complexity of Neural Networks." Neural Information Processing Systems, 1992.Markdown
[Murata et al. "Learning Curves, Model Selection and Complexity of Neural Networks." Neural Information Processing Systems, 1992.](https://mlanthology.org/neurips/1992/murata1992neurips-learning/)BibTeX
@inproceedings{murata1992neurips-learning,
title = {{Learning Curves, Model Selection and Complexity of Neural Networks}},
author = {Murata, Noboru and Yoshizawa, Shuji and Amari, Shun-ichi},
booktitle = {Neural Information Processing Systems},
year = {1992},
pages = {607-614},
url = {https://mlanthology.org/neurips/1992/murata1992neurips-learning/}
}