Best-First Model Merging for Dynamic Learning and Recognition
Abstract
"Best-first model merging" is a general technique for dynamically choosing the structure of a neural or related architecture while avoid(cid:173) ing overfitting. It is applicable to both leaming and recognition tasks and often generalizes significantly better than fixed structures. We dem(cid:173) onstrate the approach applied to the tasks of choosing radial basis func(cid:173) tions for function learning, choosing local affine models for curve and constraint surface modelling, and choosing the structure of a balltree or bumptree to maximize efficiency of access.
Cite
Text
Omohundro. "Best-First Model Merging for Dynamic Learning and Recognition." Neural Information Processing Systems, 1991.Markdown
[Omohundro. "Best-First Model Merging for Dynamic Learning and Recognition." Neural Information Processing Systems, 1991.](https://mlanthology.org/neurips/1991/omohundro1991neurips-bestfirst/)BibTeX
@inproceedings{omohundro1991neurips-bestfirst,
title = {{Best-First Model Merging for Dynamic Learning and Recognition}},
author = {Omohundro, Stephen M.},
booktitle = {Neural Information Processing Systems},
year = {1991},
pages = {958-965},
url = {https://mlanthology.org/neurips/1991/omohundro1991neurips-bestfirst/}
}