Improving the Efficiency of a User-Driven Learning System With Reconfigurable Hardware.: Application to Dna Splicing.

Abstract

This paper describes a new approach to problem solving by splitting up problem component parts between software and hardware. Our main idea arises from the combination of two previously published works. The rst one proposed a conceptual environment of concept modelling in which the machine and the human expert interact. The second one reported an algorithm based on recon gurable hardware system which outperforms any kind of previously published genetic data base scanning hardware or algorithms. Here we show h o w e cient the interaction between the machine and the expert is when the concept modelling is based on recon gurable hardware system. Their cooperation is thus achieved with an real time interaction speed. The designed system has been partially applied to the recognition of primate splice junctions sites in genetic sequences.