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- W2039352468 abstract "ABSTRACT TheMLP Iterative Construction Algorithm (MICA) designs a Multi-Layer Perceptron (MLP) neural network as it trains. MICA adds Hidden Layer Nodes (HLNs) one at a time, separating classes on a pair-wise basis, untilthe data is projected into a linear separable space by class. Then MICA trains the Output Layer Nodes (OLNs),which results in an MLP that achieves 100% accuracy on the training data. MICA, like Backprop,'2 produces an MLP that is a minimum mean squared error approximation of the Bayes optimal discriminant function. Moreover,MICA's training technique yields novel feature selection technique and hidden node pruning technique. 1 INTRODUCTION The MLP Iterative Construction Algorithm (MICA) constructs a Multi-Layer Perceptron (MLP) neural net-work for solving classification problems. MICA trains the Hidden Layer Nodes (HLNs), then trains the Output Layer Nodes (OLNs). The resulting MLP network correctly classifies the training data to 100% accuracy. MICA generalization results compare favorably to Backprop's. On difficult training sets such as spiral data, MICA is" @default.
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- W2039352468 date "1997-04-04" @default.
- W2039352468 modified "2023-09-23" @default.
- W2039352468 title "<title>MLP iterative construction algorithm</title>" @default.
- W2039352468 doi "https://doi.org/10.1117/12.271467" @default.
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