Non-linear gating network for the large scale classification model CombNET-II
Maurício Kugler, Toshiyuki Miyatani, Susumu Kuroyanagi, Anto Satriyo Nugroho, Akira Iwata · The European Symposium on Artificial Neural Networks · 2005
The linear gating classifier (stem network) of the large scale model CombNET-II has been always the limiting factor which restricts the number of the expert classifiers (branch networks). The linear boundaries between its clusters cause a rapid decrease in the performance with increas- ing number of clusters and, consequently, impair the overall performance. This work proposes the use of a non-linear classifier to learn the complex boundaries between the clusters, which increases the gating performance while keeping the balanced split of samples produced by the original se- quential clustering algorithm. The experiments have shown that, for some problems, the proposed model outperforms the monolithic classifier.