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Neural network models have recently been very popular in artificial intelligence, cognitive psychology, pattern recognition, etc. They appear to work remarkably well even for problems for which conventional statistical methods typically fail. However, how they achieve what they achieve is not understood sufficiently well. Feed-forward networks may be viewed as approximating (nonlinear) functions that connects inputs to outputs. We explore methods to analyze how the approximations are done. These methods range from a simple graphing technique to two-way and three-way constrained and unconstrained principal component analyses.


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