A new neural network based pattern recognition algorithm is proposed. The method consists in preprocessing the multidimensional data, using a space-filling curve based transformation into the unit interval, and employing Kohonen's vector quantization algorithms (of SOM and LVQ types) in one dimension. The space-filling based transformation preserves the theoretical Bayes risk. Experiments show that such an approach can produce good or even better error rates than the classical LVQ performed in a multidimensional space.
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A new approach to the standard problem of searching for optimal experimental designs is considered. It consists in replacing a multidimensional search for global maxima by a one-dimensional global search. The points found in this way are then transformed to the multivariate design domain by using a space-filling curve. It is shown that this approach leads to the optimal design, provided that the one-dimensional global search is reliable. An additional advantage of the proposed approach is the possibility of visualization of the model variance surface. The results are presented for the D-optimality criterion, but their extension to other criteria is not difficult.
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