In this article we approach neural networks as computational templates that travel across various sciences. Traditionally, it has been thought that models are primarily models of s...
— Several heuristic methods have been suggested for improving the generalization capability in neural network learning, most of which are concerned with a single-objective (SO) l...
The paper presents a framework called ECOS for Evolving COnnectionist Systems. ECOS evolve through incremental learning. They can accommodate any new input data, including new fea...
We investigate the effectiveness of GP-generated intelligent structures in classification tasks. Specifically, we present and use four context-free grammars to describe (1) decisi...
This paper investigates the combination of different neural network topologies for probabilistic feature extraction. On one hand, a five-layer neural network used in bottle neck f...