Accession Number : AD0443109

Title :   THE SYNTHESIS OF MACHINES WHICH LEARN WITHOUT A TEACHER,

Corporate Author : STANFORD UNIV CA STANFORD ELECTRONICS LABS

Personal Author(s) : Fralick, S. C.

Report Date : APR 1964

Pagination or Media Count : 19

Abstract : Techniques of decision theory are applied to the problem of learning to recognize patterns without a teacher. As a result a generalized a posteriori probability computer is obtained which includes the solution of the problem of learning without a teacher, learning with a teacher, and no learning. The resulting equations are shown to describe a system which may be synthesized in delay feedback form, of fixed size, which is stable and converges to that system which would be optimum if a priori knowledge was available so that no learning was required. The solution is used to synthesize three systems in black box form: (1) a general system which learns to make binary decisions, a specific example of this system, and (3) a general system which learns to make multiple-category classifications. (Author)

Descriptors :   (*TEACHING MACHINES, DECISION THEORY), PATTERN RECOGNITION, FEEDBACK, SYNTHESIS, BINARY ARITHMETIC, DIGITAL COMPUTERS, LEARNING.

Distribution Statement : APPROVED FOR PUBLIC RELEASE