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A UNIFYING MATHEMATICAL THEORY FOR TRAINING LEARNING NETS

机译:一种统一学习网络的统一数学理论

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This report analyzes the deterministic approaches which have been applied to the description of neural net configurations and their training algorithms which employ a single lager of trainable gaim elements and partition the input space by hyperplanes. The nets are described by n-dimensional geometric vector methods. A general algorithm is developed based on gradient or steepest-descent methods for optimizing a system given a quadratic index of performance.

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