Now showing items 1-3 of 3

  • Consultant-2: Pre- and post-processing of machine learning applications. 

    Sleeman, D.; Rissakis, M.; Craw, Susan; Graner, N.; Sharma, S. (Elsevier http://dx.doi.org/10.1006/ijhc.1995.1035, 1995-07)
    SLEEMAN, D., RISSAKIS, M., CRAW, S., GRANER, N. and SHARMA, S., 1995. Consultant-2: Pre- and post processing of machine learning applications. International Journal of Human Computer Studies, 43 (1), pp. 43-63
    The knowledge acquisition bottleneck in the development of large knowledge-based applications has not yet been resolved. One approach which has been advocated is the systematic use of Machine Learning (ML) techniques. ...
  • Knowledge modelling for a generic refinement framework 

    Boswell, Robin; Craw, Susan (Elsevier http://dx.doi.org/10.1016/S0950-7051(99)00018-0, 1999-10)
    BOSWELL, R. and CRAW, S., 1999. Knowledge modelling for a generic refinement framework. Knowledge Based Systems, 12 (5-6), pp. 317-325
    Refinement tools assist with debugging the knowledge-based system (KBS), thus easing the well-known knowledge acquisition bottleneck, and the more recently recognised maintenance overhead. The existing refinement tools ...
  • Learning adaptation knowledge to improve case-based reasoning 

    Craw, Susan; Wiratunga, Nirmalie; Rowe, Ray (Elsevier http://dx.doi.org/10.1016/j.artint.2006.09.001, 2006-11)
    CRAW, S., WIRATUNGA, N. and ROWE, R., 2006. Learning adaptation knowledge to improve case-based reasoning. Artificial Intelligence, 170 (16-17), pp. 1175-1192.
    Case-Based Reasoning systems retrieve and reuse solutions for previously solved problems that have been encountered and remembered as cases. In some domains, particularly where the problem solving is a classification task, ...