Metamorphosis Networks: An Alternative to Constructive Models
Abstract
Given a set oft raining examples, determining the appropriate num(cid:173) ber of free parameters is a challenging problem. Constructive learning algorithms attempt to solve this problem automatically by adding hidden units, and therefore free parameters, during learn(cid:173) ing. We explore an alternative class of algorithms-called meta(cid:173) morphosis algorithms-in which the number of units is fixed, but the number of free parameters gradually increases during learning. The architecture we investigate is composed of RBF units on a lat(cid:173) tice, which imposes flexible constraints on the parameters of the network. Virtues of this approach include variable subset selec(cid:173) tion, robust parameter selection, multiresolution processing, and interpolation of sparse training data.
Cite
Text
Bonnlander and Mozer. "Metamorphosis Networks: An Alternative to Constructive Models." Neural Information Processing Systems, 1992.Markdown
[Bonnlander and Mozer. "Metamorphosis Networks: An Alternative to Constructive Models." Neural Information Processing Systems, 1992.](https://mlanthology.org/neurips/1992/bonnlander1992neurips-metamorphosis/)BibTeX
@inproceedings{bonnlander1992neurips-metamorphosis,
title = {{Metamorphosis Networks: An Alternative to Constructive Models}},
author = {Bonnlander, Brian V. and Mozer, Michael},
booktitle = {Neural Information Processing Systems},
year = {1992},
pages = {131-138},
url = {https://mlanthology.org/neurips/1992/bonnlander1992neurips-metamorphosis/}
}