A family of projection learnings
Akira Hirabayashi, Hidemitsu Ogawa · Systems and Computers in Japan · 2001
Abstract This paper discusses the learning problem of obtaining good generalization capability from a given set of training examples when the space to which a target function belongs is known. One of the present authors, H. Ogawa, proposed the concepts of projection learning (PL), partial projection learning (PTPL), and averaged projection learning (APL). He also devised the concept of a family of projection learnings consisting of infinitely many kinds of learnings including PL, PTPL, and APL. It provides a framework to discuss infinite kinds of learnings in a unified way. Although a couple of definitions were given for the family of projection learnings, they are still unsatisfactory for further development of the theory. In this paper, we propose a more natural definition of the family of projection learnings and consolidate a foundation of the theory of a family of projection learnings. © 2001 Scripta Technica, Syst Comp Jpn, 32(5): 21–35, 2001