Radial Basis Function Network for Multi-Task Learning
Abstract
We extend radial basis function (RBF) networks to the scenario in which multiple correlated tasks are learned simultaneously, and present the cor- responding learning algorithms. We develop the algorithms for learn- ing the network structure, in either a supervised or unsupervised manner. Training data may also be actively selected to improve the network’s gen- eralization to test data. Experimental results based on real data demon- strate the advantage of the proposed algorithms and support our conclu- sions.
Cite
Text
Liao and Carin. "Radial Basis Function Network for Multi-Task Learning." Neural Information Processing Systems, 2005.Markdown
[Liao and Carin. "Radial Basis Function Network for Multi-Task Learning." Neural Information Processing Systems, 2005.](https://mlanthology.org/neurips/2005/liao2005neurips-radial/)BibTeX
@inproceedings{liao2005neurips-radial,
title = {{Radial Basis Function Network for Multi-Task Learning}},
author = {Liao, Xuejun and Carin, Lawrence},
booktitle = {Neural Information Processing Systems},
year = {2005},
pages = {792-802},
url = {https://mlanthology.org/neurips/2005/liao2005neurips-radial/}
}