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This paper studies the performance associated with the classification of linear subspaces corrupted by noise with a mismatched classifier. In particular, we consider a problem where the classifier observes a noisy signal, the signal distribution conditioned on the signal class is zero-mean Gaussian with low-rank covariance matrix, and the classifier knows only the mismatched parameters in lieu of...
The ART-based neural networks summarize data into groups via the use of inner categories. A category's template elements are updated incrementally in the light of new evidence provided by the presentation of input patterns. In order to reduce approximation error, this paper proposes Bayesian Polytope ARTMAP (BPTAM) which incorporates both simplex categories and Gaussian categories. During training,...
This work consists on the evaluation of the performances of three neural classifiers. The Multi-Layer Perceptron (MLP), the Self-Organizing Map (SOM), the Learning Vector Quantization (LV Q) are considered by this study. The example that will be considered in the evaluation of the technical classifications's performances is the handwritten character recognition.
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