APPLICATIONS OF MACHINE LEARNING - TOWARDS KNOWLEDGE SYNTHESIS

This paper shows, by discussing a number of Machine Learning (ML) applications, that the existing ML techniques can be effectively applied in knowledge acquisition for expert systems, thereby alleviating the known knowledge acquisition bottleneck. Analysis in domains of practical interest indicates that the performance accuracy of knowledge induced through learning from examples compares very favourably with the accuracy of best human experts. Also, in addition to accuracy, there are encouraging examples regarding the clarity and meaningfulness of induced knowledge. This points towards automated knowledge synthesis, although much further research is needed in this direction. The state of the art of some approaches to Machine Learning is assessed relative to their practical applicability and the characteristics of a problem domain.