In this paper, we propose two new neuro-fuzzy schemes, one for classification
and one for clustering problems. The classification scheme is based
on Simpson's fuzzy min-max method (1992, 1993) and relaxes some assumptions
he makes. This enables our scheme to handle mutually nonexclusive
classes. The neuro-fuzzy clustering scheme is a multiresolution algorithm
that is modeled after the mechanics of human pattern recognition.
We also present data from an exhaustive comparison of these techniques
with neural, statistical, machine learning, and other traditional
approaches to pattern recognition applications. The data sets used
for comparisons include those from the machine learning repository
at the University of California, Irvine. We find that our proposed
schemes compare quite well with the existing techniques, and in addition
offer the advantages of one-pass learning and online adaptation
%0 Journal Article
%1 Joshi1997
%A Joshi, A.
%A Ramakrishman, N.
%A Houstis, E.N.
%A Rice, J.R.
%D 1997
%J Neural Networks, IEEE Transactions on
%K (artificial adaptation, algorithm, analysis, classes, classification, clustering, fuzzy human intelligence), learning learning, machine method, min-max minimax multiresolution mutually nets, neural neuro-fuzzy neurobiological nonexclusive one-pass online pattern recognition recognition, set statistical techniques, theory,
%N 1
%P 18--31
%T On neurobiological, neuro-fuzzy, machine learning, and statistical
pattern recognition techniques
%V 8
%X In this paper, we propose two new neuro-fuzzy schemes, one for classification
and one for clustering problems. The classification scheme is based
on Simpson's fuzzy min-max method (1992, 1993) and relaxes some assumptions
he makes. This enables our scheme to handle mutually nonexclusive
classes. The neuro-fuzzy clustering scheme is a multiresolution algorithm
that is modeled after the mechanics of human pattern recognition.
We also present data from an exhaustive comparison of these techniques
with neural, statistical, machine learning, and other traditional
approaches to pattern recognition applications. The data sets used
for comparisons include those from the machine learning repository
at the University of California, Irvine. We find that our proposed
schemes compare quite well with the existing techniques, and in addition
offer the advantages of one-pass learning and online adaptation
@article{Joshi1997,
abstract = {In this paper, we propose two new neuro-fuzzy schemes, one for classification
and one for clustering problems. The classification scheme is based
on Simpson's fuzzy min-max method (1992, 1993) and relaxes some assumptions
he makes. This enables our scheme to handle mutually nonexclusive
classes. The neuro-fuzzy clustering scheme is a multiresolution algorithm
that is modeled after the mechanics of human pattern recognition.
We also present data from an exhaustive comparison of these techniques
with neural, statistical, machine learning, and other traditional
approaches to pattern recognition applications. The data sets used
for comparisons include those from the machine learning repository
at the University of California, Irvine. We find that our proposed
schemes compare quite well with the existing techniques, and in addition
offer the advantages of one-pass learning and online adaptation},
added-at = {2009-09-12T19:19:34.000+0200},
author = {Joshi, A. and Ramakrishman, N. and Houstis, E.N. and Rice, J.R.},
biburl = {https://www.bibsonomy.org/bibtex/20e5cc5126f20c032058901d49aef26e9/mozaher},
file = {00554188.pdf:concept disambiguation\\context\\20070222\\00554188.pdf:PDF},
interhash = {d21212dee91936e54138043f16faef22},
intrahash = {0e5cc5126f20c032058901d49aef26e9},
issn = {1045-9227},
journal = {Neural Networks, IEEE Transactions on},
keywords = {(artificial adaptation, algorithm, analysis, classes, classification, clustering, fuzzy human intelligence), learning learning, machine method, min-max minimax multiresolution mutually nets, neural neuro-fuzzy neurobiological nonexclusive one-pass online pattern recognition recognition, set statistical techniques, theory,},
number = 1,
owner = {Mozaher},
pages = {18--31},
timestamp = {2009-09-12T19:19:40.000+0200},
title = {On neurobiological, neuro-fuzzy, machine learning, and statistical
pattern recognition techniques},
volume = 8,
year = 1997
}