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Visual classification of medical data using MLP mapping
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DOI：10.1016/S0010-4825(98)00010-9.png)
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Abstract
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In this work we discuss the design of a novel non-linear mapping method for visual classification based on multilayer perceptrons (MLP) and assigned class target values. In training the perceptron, one or more target output values for each class in a 2-dimensional space are used. In other words, class membership information is interpreted visually as closeness to target values in a 2D feature space. This mapping is obtained by training the multilayer perceptron (MLP) using class membership information, input data and judiciously chosen target values. Weights are estimated in such a way that each training feature of the corresponding class is forced to be mapped onto the corresponding 2-dimensional target value. (C) 1998 Elsevier Science Ltd. All rights reserved.
Keywords:
visual displays
multilayer perceptrons
respiratory sound patterns
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