Engineering topics

Schlimmer, Jeffrey C.

Publications and source records attributed to Schlimmer, Jeffrey C..

2 records

NASA NTRS1994

A machine-learning apprentice for the completion of repetitive forms

Forms of all types are used in businesses and government agencies, and most of them are filled in by hand. Yet much time and effort has been expended to automate form-filling by programming specific systems or computers. The high cost of programmers and other resources prohibits many organizations from benefiting from efficient office automation. A learning apprentice can be used for such repetitious form-filling tasks. In this paper, we establish the need for learning apprentices, describe a framework for such a system, explain the difficulties of form-filling, and present empirical results of a form-filling system used in our department from September 1991 to April 1992. The form-filling apprentice saves up to 87 percent in keystroke effort and correctly predicts nearly 90 percent of the values on the form.

Hermens, Leonard A.↗

NASA NTRS1991

Paradigms for machine learning

Five paradigms are described for machine learning: connectionist (neural network) methods, genetic algorithms and classifier systems, empirical methods for inducing rules and decision trees, analytic learning methods, and case-based approaches. Some dimensions are considered along with these paradigms vary in their approach to learning, and the basic methods are reviewed that are used within each framework, together with open research issues. It is argued that the similarities among the paradigms are more important than their differences, and that future work should attempt to bridge the existing boundaries. Finally, some recent developments in the field of machine learning are discussed, and their impact on both research and applications is examined.

Schlimmer, Jeffrey C.↗