Auditory evoked potential classification by unsupervised ART 2-A and supervised fuzzy ARTMAP networks

Alpsan, D.

doi:10.1109/icnn.1994.374900

Cited by 6 publications

(2 citation statements)

References 8 publications

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“…16,[18][19][20][21][22][35][36][37][38][39] 3. EXPERIMENTAL SECTION ART 2-A was applied on a data matrix of 103 aliphatic substituents ( Table 1) described by the hydrophobic constant for aliphatic substituents Fr, H-bonding acceptor (HBA) and donor (HBD) abilities, the molar refractivity (MR), and the inductive parameter F, respectively.…”

Section: Theory Of Art 2-amentioning

confidence: 99%

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ART 2-A for Optimal Test Series Design in QSAR

Domine

Devillers

Wienke

et al. 1997

J. Chem. Inf. Comput. Sci.

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The family of adaptive resonance theory (ART) based systems concerns distinct artificial neural networks for unsupervised and supervised clustering analysis. Among them, the ART 2-A paradigm presents numerous strengths for data analysis. After a rapid presentation of the ART 2-A theory and algorithmic information, the usefulness of this neural network for the selection of optimal test series is estimated. The results are compared with those obtained from hierarchical cluster analysis and visual mapping methods. The advantages and drawbacks of each method are discussed. We show that ART 2-A represents a new useful nonlinear statistical tool for QSAR and drug design.

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Section: Theory Of Art 2-amentioning

confidence: 99%

“…Only a few papers dealing with applications of ART 2-A can be found in the literature, but they can provide complementary information on the ART 2-A paradigm and can represent a source of ideas for the development of ART 2-A applications. ,− ,− …”

Section: Theory Of Art 2-amentioning

confidence: 99%