Neural networks can approximate mappings on structured objects
Barbara Hammer, Jana Sperschneider · 1997
The folding architecture is a universal mechanism to approximate mappings between trees and real vector spaces with a neural network. The part encoding the trees and the part approximating the mapping are trained simultaneously so that the encoding fits to the specific learning task. In this article we show that this architecture is capable of approximating any mapping arbitrary well.