Thivierge, J.-P., & Shultz, T. R. (2003). Information networks with modular experts. In M.H. Hamza (Ed.), IASTED Artificial Intelligence and Applications (pp. 753-758). Zurich.

 

Abstract

Information networks learn by adjusting the amount of information fed to the hidden units. This technique can be expanded to manipulate the amount of information fed to modular experts in a network’s architecture. After generating experts that vary in relevance, we show that competition among them can be obtained by information maximization. After generating equally relevant but diverging experts using AdaBoost, collaboration among them can be obtained by constrained information maximization. By controlling the amount of information fed to the experts, we can outperform a number of other mixture models on real world data.

 

Copyright notice

Abstracts, papers, chapters, and other documents are posted on this site as an efficient way to distribute reprints. The respective authors and publishers of these works retain all of the copyrights to this material. Anyone copying, downloading, bookmarking, or printing any of these materials agrees to comply with all of the copyright terms. Other than having an electronic or printed copy for fair personal use, none of these works may be reposted, reprinted, or redistributed without the explicit permission of the relevant copyright holders.

 

To obtain a PDF reprint of this particular article, signal your agreement with these copyright terms by clicking on the statement below.

 

I agree with all of these copyright terms PDF 140KB