Bumptrees for Efficient Function, Constraint and Classification Learning
Abstract
A new class of data structures called "bumptrees" is described. These structures are useful for efficiently implementing a number of neural network related operations. An empirical comparison with radial basis functions is presented on a robot ann mapping learning task. Applica(cid:173) tions to density estimation. classification. and constraint representation and learning are also outlined.
Cite
Text
Omohundro. "Bumptrees for Efficient Function, Constraint and Classification Learning." Neural Information Processing Systems, 1990.Markdown
[Omohundro. "Bumptrees for Efficient Function, Constraint and Classification Learning." Neural Information Processing Systems, 1990.](https://mlanthology.org/neurips/1990/omohundro1990neurips-bumptrees/)BibTeX
@inproceedings{omohundro1990neurips-bumptrees,
title = {{Bumptrees for Efficient Function, Constraint and Classification Learning}},
author = {Omohundro, Stephen M.},
booktitle = {Neural Information Processing Systems},
year = {1990},
pages = {693-699},
url = {https://mlanthology.org/neurips/1990/omohundro1990neurips-bumptrees/}
}