Order of search in Fuzzy ART and Fuzzy ARTMAP: Effect of the choice parameter

Michael Georgiopoulos; Hans Fernlund; George Bebis; Gregory L. Heileman

doi:10.1016/S0893-6080(96)00018-4

Order of search in Fuzzy ART and Fuzzy ARTMAP: Effect of the choice parameter

Michael Georgiopoulos, Hans Fernlund, George Bebis, Gregory L. Heileman

Research output: Contribution to journal › Article › peer-review

64 Scopus citations

Abstract

This paper focuses on two ART architectures, the Fuzzy ART and the Fuzzy ARTMAP. Fuzzy ART is a pattern clustering machine, while Fuzzy ARTMAP is a pattern classification machine. Our study concentrates on the order according to which categories in Fuzzy ART, or the ART(a) model of Fuzzy ARTMAP are chosen. Our work provides a geometrical, and clearer understanding of why, and in what order, these categories are chosen for various ranges of the choice parameter of the Fuzzy ART module. This understanding serves as a powerful tool in developing properties of learning pertaining to these neural network architectures; to strengthen this argument, it is worth mentaining that the order according to which categories are chosen in ART 1 and ARTMAP provided a valuable tool in proving important properties about these architectures.

Original language	English (US)
Pages (from-to)	1541-1559
Number of pages	19
Journal	Neural Networks
Volume	9
Issue number	9
DOIs	https://doi.org/10.1016/S0893-6080(96)00018-4
State	Published - Dec 1996
Externally published	Yes

Keywords

Fuzzy ART
Fuzzy ARTMAP
adaptive resonance theory
classification
clustering
learning
neural network

ASJC Scopus subject areas

Cognitive Neuroscience
Artificial Intelligence

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10.1016/S0893-6080(96)00018-4

Cite this

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title = "Order of search in Fuzzy ART and Fuzzy ARTMAP: Effect of the choice parameter",

abstract = "This paper focuses on two ART architectures, the Fuzzy ART and the Fuzzy ARTMAP. Fuzzy ART is a pattern clustering machine, while Fuzzy ARTMAP is a pattern classification machine. Our study concentrates on the order according to which categories in Fuzzy ART, or the ART(a) model of Fuzzy ARTMAP are chosen. Our work provides a geometrical, and clearer understanding of why, and in what order, these categories are chosen for various ranges of the choice parameter of the Fuzzy ART module. This understanding serves as a powerful tool in developing properties of learning pertaining to these neural network architectures; to strengthen this argument, it is worth mentaining that the order according to which categories are chosen in ART 1 and ARTMAP provided a valuable tool in proving important properties about these architectures.",

keywords = "Fuzzy ART, Fuzzy ARTMAP, adaptive resonance theory, classification, clustering, learning, neural network",

author = "Michael Georgiopoulos and Hans Fernlund and George Bebis and Heileman, {Gregory L.}",

note = "Funding Information: This research was supported in part by a grant from Boeing Computer Services under Contract W-300445. ",

year = "1996",

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doi = "10.1016/S0893-6080(96)00018-4",

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T1 - Order of search in Fuzzy ART and Fuzzy ARTMAP

T2 - Effect of the choice parameter

AU - Georgiopoulos, Michael

AU - Fernlund, Hans

AU - Bebis, George

AU - Heileman, Gregory L.

N1 - Funding Information: This research was supported in part by a grant from Boeing Computer Services under Contract W-300445.

PY - 1996/12

Y1 - 1996/12

N2 - This paper focuses on two ART architectures, the Fuzzy ART and the Fuzzy ARTMAP. Fuzzy ART is a pattern clustering machine, while Fuzzy ARTMAP is a pattern classification machine. Our study concentrates on the order according to which categories in Fuzzy ART, or the ART(a) model of Fuzzy ARTMAP are chosen. Our work provides a geometrical, and clearer understanding of why, and in what order, these categories are chosen for various ranges of the choice parameter of the Fuzzy ART module. This understanding serves as a powerful tool in developing properties of learning pertaining to these neural network architectures; to strengthen this argument, it is worth mentaining that the order according to which categories are chosen in ART 1 and ARTMAP provided a valuable tool in proving important properties about these architectures.

AB - This paper focuses on two ART architectures, the Fuzzy ART and the Fuzzy ARTMAP. Fuzzy ART is a pattern clustering machine, while Fuzzy ARTMAP is a pattern classification machine. Our study concentrates on the order according to which categories in Fuzzy ART, or the ART(a) model of Fuzzy ARTMAP are chosen. Our work provides a geometrical, and clearer understanding of why, and in what order, these categories are chosen for various ranges of the choice parameter of the Fuzzy ART module. This understanding serves as a powerful tool in developing properties of learning pertaining to these neural network architectures; to strengthen this argument, it is worth mentaining that the order according to which categories are chosen in ART 1 and ARTMAP provided a valuable tool in proving important properties about these architectures.

KW - Fuzzy ART

KW - Fuzzy ARTMAP

KW - adaptive resonance theory

KW - classification

KW - clustering

KW - learning

KW - neural network

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Order of search in Fuzzy ART and Fuzzy ARTMAP: Effect of the choice parameter

Abstract

Keywords

ASJC Scopus subject areas

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