Family Discovery
Abstract
"Family discovery" is the task of learning the dimension and struc(cid:173) ture of a parameterized family of stochastic models. It is espe(cid:173) cially appropriate when the training examples are partitioned into "episodes" of samples drawn from a single parameter value. We present three family discovery algorithms based on surface learn(cid:173) ing and show that they significantly improve performance over two alternatives on a parameterized classification task.
Cite
Text
Omohundro. "Family Discovery." Neural Information Processing Systems, 1995.Markdown
[Omohundro. "Family Discovery." Neural Information Processing Systems, 1995.](https://mlanthology.org/neurips/1995/omohundro1995neurips-family/)BibTeX
@inproceedings{omohundro1995neurips-family,
title = {{Family Discovery}},
author = {Omohundro, Stephen M.},
booktitle = {Neural Information Processing Systems},
year = {1995},
pages = {402-408},
url = {https://mlanthology.org/neurips/1995/omohundro1995neurips-family/}
}