RECENT ADVANCES IN MACHINE LEARNING
Yves Kodratoff · International Journal of Pattern Recognition and Artificial Intelligence · 1992
Inference is a very general reasoning process that allows us to draw consequences from some body of knowledge. Machine learning (ML) uses the three kinds of possible inferences, deductive, inductive, and analogical. We describe here different methods, using these inferences, that have been created during the last decade to improve the way machines can learn. We have already presented the most classical approaches in a book (Kodratoff, 1988), and in several review papers (Kodratoff, 1989, 1990a, 1992). These results will be described here very briefly, in order to leave room for newer results. We include also genetic algorithms as an induction technique. We restrict our presentation to the symbolic aspects of connectionism. A learning system can also be viewed as a mechanism skimming interesting knowledge out of the flow of information that runs through it. We present several existing learning systems from this point of view.