SOME METHODOLOGICAL ASPECTS OF MACHINE LEARNING
Jesús G. Boticario, José Mira · Cybernetics & Systems · 1994
There is a lack of theory and methodology for the development and proper evaluation of the machine learning (ML) applications built so far. This paper presents some methodological considerations concerning a particular ML perspective expressed in a methodological model called 2D-L1M, a multilevel paradigm-independent methodology that is intended to help during the continuous specification mechanism implicitly present on developing learning programs. Taking into account the striking differences between the knowledge level description of these systems and the symbolic ones, we have identified new layers for their characterization, from the observer point of view and from a pure computational perspective. Our proposal is supported with symbolic structures and arguments from distinguished machine learning strategies and applications (Winston et al., 1983; Mitchell et al, 1983, 1986; Dejong and Mooney, 1986; Carbonell, 1986; Quinlan, 1986; Hammond, 1986; Minton, 1990; Bergadano and Giordana, 1988; Hirsh 1990) and from our own experience in the field (Boticario, 1988; Dent et al., 1992).