| Popis: |
A generalist-specialist paradigm for building neural networks is presented. The underlying principle is derived from an analogy with medicine. In most cases, a general practitioner or generalist is able to make a sure diagnosis: however, the generalist sometimes hesitates between several diagnoses and chooses to send his patient to see one or several specialists. This neural network architecture offers many advantages: improvement of the network performances, enhancement of the network interpretation, and selection of the critical frontiers between attraction valleys. The design of specialists is strongly dependent on the ambiguity threshold. A small threshold will lead to a rather simple network, whereas a larger value ensures a better robustness for the global network. This design methodology allows one to adjust the architecture's complexity to the overall problem's complexity. The authors present the definition of ambiguities matrices for the generalist networks, discuss methods for constructing specialists, and examine the functioning of the overall network |
