Shultz, T. R., & Oshima-Takane, Y. (1994). Analysis of unscaled contributions in cross connected networks. Proceedings of the World Congress on Neural Networks (Vol. 3, pp. 690-695). Hillsdale, NJ: Erlbaum.



Contribution analysis is a useful tool for the analysis of cross-connected networks such as those generated by the cascade-correlation learning algorithm. Networks with cross connections that supersede hidden layers pose particular difficulties for standard analyses of hidden unit activation patterns. A contribution is defined as the product of an output weight and the associated activation on the sending unit. Previously such contributions have been multiplied by the sign of the output target for a particular input pattern. The present work shows that a principal components analysis (PCA) of unscaled contributions yields more interesting insights than comparable analyses of contributions scaled by the sign of output targets.


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