Adaptively Growing Hierarchical Mixtures of Experts
Abstract
We propose a novel approach to automatically growing and pruning Hierarchical Mixtures of Experts. The constructive algorithm pro(cid:173) posed here enables large hierarchies consisting of several hundred experts to be trained effectively. We show that HME's trained by our automatic growing procedure yield better generalization per(cid:173) formance than traditional static and balanced hierarchies. Eval(cid:173) uation of the algorithm is performed (1) on vowel classification and (2) within a hybrid version of the JANUS r9] speech recog(cid:173) nition system using a subset of the Switchboard large-vocabulary speaker-independent continuous speech recognition database.
Cite
Text
Fritsch et al. "Adaptively Growing Hierarchical Mixtures of Experts." Neural Information Processing Systems, 1996.Markdown
[Fritsch et al. "Adaptively Growing Hierarchical Mixtures of Experts." Neural Information Processing Systems, 1996.](https://mlanthology.org/neurips/1996/fritsch1996neurips-adaptively/)BibTeX
@inproceedings{fritsch1996neurips-adaptively,
title = {{Adaptively Growing Hierarchical Mixtures of Experts}},
author = {Fritsch, Jürgen and Finke, Michael and Waibel, Alex},
booktitle = {Neural Information Processing Systems},
year = {1996},
pages = {459-465},
url = {https://mlanthology.org/neurips/1996/fritsch1996neurips-adaptively/}
}