Incremental Active Learning for Optimal Generalization
Abstract
The problem of designing input signals for optimal generalization is called active learning. In this article, we give a two-stage sampling scheme for reducing both the bias and variance, and based on this scheme, we propose two active learning methods. One is the multipoint search method applicable to arbitrary models. The effectiveness of this method is shown through computer simulations. The other is the optimal sampling method in trigonometric polynomial models. This method precisely specifies the optimal sampling locations.
Cite
Text
Sugiyama and Ogawa. "Incremental Active Learning for Optimal Generalization." Neural Computation, 2001. doi:10.1162/089976600300014773Markdown
[Sugiyama and Ogawa. "Incremental Active Learning for Optimal Generalization." Neural Computation, 2001.](https://mlanthology.org/neco/2001/sugiyama2001neco-incremental/) doi:10.1162/089976600300014773BibTeX
@article{sugiyama2001neco-incremental,
title = {{Incremental Active Learning for Optimal Generalization}},
author = {Sugiyama, Masashi and Ogawa, Hidemitsu},
journal = {Neural Computation},
year = {2001},
pages = {2909-2940},
doi = {10.1162/089976600300014773},
volume = {12},
url = {https://mlanthology.org/neco/2001/sugiyama2001neco-incremental/}
}