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Average performance of fuzzy ARTMAP (with MT+, MT-, WMT and PSO(MT)) versus training subset size for NIST SD19 data set. Error bars are standard error of the sample mean.

Average performance of fuzzy ARTMAP (with MT+, MT-, WMT and PSO(MT)) versus training subset size for NIST SD19 data set. Error bars are standard error of the sample mean.

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Training fuzzy ARTMAP neural networks for classification using data from com-plex real-world environments may lead to category proliferation, and yield poor performance. This problem is known to occur whenever the training set contains noisy and overlapping data. Moreover, when the training set contains identical input patterns that belong to diffe...

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Context 1
... contrast, when decision boundaries are complex, these ε values tend towards 0. 4.3. NIST SD19 data: Figure 10 presents the average performance obtained when fuzzy ARTMAP is trained on the NIST SD19 data using the four MT strategies -MT-, MT+, WMT and PSO(MT). The generalisation error for the k-NN classifier are also shown for reference. ...
Context 2
... shown in Figure 10(d), when α = 0.001, β = 1 and ρ = 0, and decision boundaries are complex, the values of ε that minimize error tends from about -0.2 towards 0 as the training set size grows. As with D CIS and D P2 , Generalisation error of PSO(MT) tends toward that of to MT+ and MT-on this data set. ...