Knowledge Extraction from Local Function Networks
Proceedings of the International Joint Conference on Artificial Intelligence,
- Aug 2001
Associated documents :
Extracting rules from RBFs is not a trivial task
because of nonlinear functions or high input dimensionality. In such cases, some of the hidden
units of the RBF network have a tendency to be
shared across several output classes or even may
not contribute to any output class. To address this
we have developed an algorithm called LREX (for
Local Rule EXtraction) which tackles these issues
by extracting rules at two levels: hREX extracts
rules by examining the hidden unit to class assignments while mREX extracts rules based on the input space to output space mappings. The rules extracted by our algorithm are compared and contrasted against a competing local rule extraction
system. The central claim of this paper is that local function networks such as radial basis function
(RBF) networks have a suitable architecture based
on Gaussian functions that is amenable to rule extraction
@InProceedings{MWM01a,
author = {McGarry, Ken and Wermter, Stefan and MacIntyre, J.},
title = {Knowledge Extraction from Local Function Networks},
booktitle = {Proceedings of the International Joint Conference on Artificial Intelligence},
editors = {},
number = {},
volume = {},
pages = {},
year = {2001},
month = {Aug},
publisher = {},
doi = {},
}