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Abstract: One of the properties of FAM, which can be both an asset and a liability, is its capacity to produce new neurons (templates) on demand to represent classification categories. This property allows FAM to automatically adapt to the database without having to arbitrarily specify network structure. This same property though has the undesirable side effect that, on large databases, it can produce a large network size that can dramatically slow down the algorithms training time. To address this... (Update)
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BibTeX entry: (Update)
@misc{ castro-data,
author = "Jose Castro and et al.",
title = "A Data Partitioning Approach to speed up the Fuzzy ARTMAP algorithm using
the Hilbert space-filling Curve",
url = "citeseer.ist.psu.edu/739129.html" }
Citations (may not include all citations):2177 Programs for Machine Learning (context) - Quinlan - 1993
145 SPRINT: A scalable parallel classifier for data mining - Shafer, Agrawal et al. - 1996
116 Fuzzy ARTMAP: A neural network architecture for incremental .. (context) - Carpenter, Grossberg et al. - 1992
111 SLIQ: A fast scalable classifier for data mining - Mehta, Agrawal et al. - 1996
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9 Comparison of neural networks and discriminant analysis in p.. (context) - Blackard - 1999
5 Fuzzy ART: An adaptive resonance algorithm for rapid, stable.. (context) - Carpenter, Grossberg et al. - 1991
5 Using space--filling curves for multi-- dimensional indexing - Lawder, King - 2000
1 Slovakia: ITAT (context) - Space, Curves et al. - 2002
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