@inproceedings{5e46f94d2c9f4266ad07239d5f568d2a,
title = "Unsupervised Splitting Rules for Neural Tree Classifiers",
abstract = "This paper presents two unsupervised neural network splitting rules for use with CART-like neural tree algorithms in high dimensional data space. These splitting rules use an adaptive variance estimate to avoid some possible local minima which arise in unsupervised methods. We explain when the unsupervised splitting rules outperform supervised neural network splitting rules and when the unsupervised splitting rules outperform the standard node impurity splitting rules of CART. Using these unsupervised splitting rules leads to a nonparametric classifier for high dimensional space that extracts local features in an optimized way.",
author = "Perrone, {Michael P.} and Nathan Intrator",
note = "Publisher Copyright: {\textcopyright} 1992 IEEE.; null ; Conference date: 07-06-1992 Through 11-06-1992",
year = "1992",
doi = "10.1109/IJCNN.1992.227216",
language = "אנגלית",
series = "Proceedings of the International Joint Conference on Neural Networks",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "820--825",
booktitle = "Proceedings - 1992 International Joint Conference on Neural Networks, IJCNN 1992",
address = "ארצות הברית",
}