An inductive learning algorithm based on regression analysis
Hiroshi Tsukimoto, Chie Morita, Nobuhiro Shimogori · Systems and Computers in Japan · 1997
There are two types of learning. One is symbol learning such as inductive learning in artificial intelligence and the other is pattern learning such as multivariate analysis and neural networking. This paper presents an inductive learning algorithm which obtains propositions whose errors are at a minimum. The algorithm is based on regression analysis and works better than C4.5. The algorithm consists of preprocessing data, obtaining linear functions by multiple regression analysis, and approximating the functions with Boolean theory. Approximating linear functions with Boolean theory is a pseudo maximum likelihood method and regression is the least square method. They are the principles of pattern learning, and so an algorithm for symbolic learning can be obtained by these principles. © 1997 Scripta Technica, Inc. Syst Comp Jpn, 28(3): 62–70, 1997