%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 }