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Optimal Algorithmic Complexity of Fuzzy ART (1998)

Burwick, T. ; Joublin, F.

Springer

Neural processing letters 7 (1998), S. 37-41

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ISSN: 1573-773X

Keywords: adaptive resonance theory ; algorithmic complexity ; fuzzy systems ; neural networks ; unsupervised learning

Source: Springer Online Journal Archives 1860-2000

Topics: Computer Science

Notes: Abstract We discuss implementations of the Adaptive Resonance Theory (ART) on a serial machine. The standard formulation of ART, which was inspired by recurrent brain structures, corresponds to a recursive algorithm. This induces an algorithmic complexity of order O(N2)+O(MN) in worst and average case, N being the number of categories, and M the input dimension. It is possible, however, to formulate ART in a non-recursive algorithm such that the complexity is of order O(MN) only.

Type of Medium: Electronic Resource

URL: http://dx.doi.org/10.1023/A:1009632604848

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