Learning Discontinuities with Products-of-Sigmoids for Switching Between Local Models
Abstract
Sensorimotor data from many interesting physical interactions comprises discontinuities. While existing locally weighted learning approaches aim at learning smooth functions, we propose a model that learns how to switch discontinuously between local models. The local responsibilities, usually represented by Gaussian kernels, are learned by a product of local sigmoidal classifiers that can represent complex shaped and sharply bounded regions. Local models are incrementally added. A locality prior constrains them to learn only local data---which is the key ingredient for incremental learning with local models.
Cite
Text
Toussaint and Vijayakumar. "Learning Discontinuities with Products-of-Sigmoids for Switching Between Local Models." International Conference on Machine Learning, 2005. doi:10.1145/1102351.1102465Markdown
[Toussaint and Vijayakumar. "Learning Discontinuities with Products-of-Sigmoids for Switching Between Local Models." International Conference on Machine Learning, 2005.](https://mlanthology.org/icml/2005/toussaint2005icml-learning/) doi:10.1145/1102351.1102465BibTeX
@inproceedings{toussaint2005icml-learning,
title = {{Learning Discontinuities with Products-of-Sigmoids for Switching Between Local Models}},
author = {Toussaint, Marc and Vijayakumar, Sethu},
booktitle = {International Conference on Machine Learning},
year = {2005},
pages = {904-911},
doi = {10.1145/1102351.1102465},
url = {https://mlanthology.org/icml/2005/toussaint2005icml-learning/}
}