Flexible resource-allocating network for noisy data
Arindam Nag, Joydeep Ghosh · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1998
The resource allocating network (RAN) provides a simple and powerful method for on-line modeling with incremental growth in model complexity. However, the network growing algorithm is susceptible to outliers in the output domain. Pruning techniques subsequently proposed for RAN, though satisfactory for dealing with outliers in the input domain, are incapable of removing units grown in response to outliers in the output domain. The addition of a coarse scale unit in response to an output outlier results in a much larger network where units are wasted to negate the effect of the spurious unit. The resulting network generalizes poorly. In this paper, we discuss the problems associated with RAN in the presence of outliers, and provide a modified learning algorithm which recognizes and prunes units associated with spurious data. We also present a strategy to modify the remaining units, once a unit is pruned.