Non-linear gating network for the large scale classification model CombNET-II

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 increasing 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 sequential clustering algorithm. The experiments have shown that, for some problems, the proposed model outperforms the monolithic classifier.