Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10326070 | Neural Networks | 2005 | 16 Pages |
Abstract
In this paper, we present sufficient conditions which guarantee the absence of local minima of the error function in the case of learning directed acyclic graphs with recursive neural networks. We introduce topological indices which can be directly calculated from the given training set and that allows us to design the neural architecture with local minima free error function. In particular, we conceive a reduction algorithm that involves both the information attached to the nodes and the topology, which enlarges significantly the class of the problems with unimodal error function previously proposed in the literature.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Marco Gori, Alessandro Sperduti,