The Learning of Parameters for Generating Compound Characterizers for Pattern Recognition

Abstract

This paper presents and describes a pattern recognition program with a relatively simple and general basic structure upon which has been superimposed a rather wide variety of techniques for learning, or self-organization. The program attempts to generalize n-tuple approaches to pattern recognition, in which ann-tuple is a set of individual cells or small pieces of patterns, and each n-tuple is said to characterize an input pattern when these pieces match it, as specified. The program allows n-tuples to match when only some of their parts match, and it allows these parts to match even though they are not precisely positioned (See Uhr, 1969b', for some simple example programs). It further learns, in a variety of ways: It searches for good weights on its characterizers' implications, by re-weighting as a function of feedback. It generates and discovers new characterizers (and can therefore begin with no characterizers at all), and discards characterizers that prove to be poor (See Uhr and Vossler, 1961, and Prather and Uhr, 1964). It also uses a set of characterizers of characterizers, to search for good parameter values that newlygenerated characterizers should have. A detailed flow-chart-like "precis" description of the program is given, along with an actual listing. It is thus possible to examine exactly what the program does, and how it does it, and therefore to see how a wide variety of learning mechanisms have been implemented in a single pattern recognition program. But because it was coded in a · "high-level" patternmatching and list-processing language the program runs too slowly for extensive tests to be practicable. Therefore only a brief listing of output is given, to show that the program, works and begins to learn. Descriptors: Learning, self-organization, induction, discovery, pattern recognition, learning to learn, n-tuple recognition, characterizing characterizers.