A neural network weight pattern study with ECG pattern recognition
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Singular-value decomposition (SVD) is used to analyze the weight pattern of a back-propagation (BP) model for efficient classification of ECG waveforms. It is found that the rank of a matrix formed by all the weights, which can be determined by SVD of that matrix, is a good indicator of the number of hidden nets needed. The SVD is also used to analyze the relationship between the weight patterns and the learned features of the input patterns.<<ETX>>
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