Accession Number:

AD0432152

Title:

CONVERGENCE PROPERTIES OF A LEARNING ALGORITHM,

Descriptive Note:

Corporate Author:

CALIFORNIA UNIV LOS ANGELES

Personal Author(s):

Report Date:

1964-01-01

Pagination or Media Count:

8.0

Abstract:

In the learning process described by the algorithm, observations are made on individuals one at a time and the current estimate of the required partitioning may be adjusted after each observation, on the basis of knowledge of the category to which the individual observed belongs. At any given time, the current estimate of the partitioning is all that is held in memory past history is lost except insofar as it has been incorporated into the present estimate. The learning process of perceptrons, as well as that of other artificial intelligence, is of this general form. It is assumed that each individual is a member of one and only one of two categories. The results obtained are applicable to the more general case, however, for they may be applied to appropriate partitions of a set of three or more categories into two subsets. Each individual in the population is characterized by an attribute vector X in m-dimensional Euclidean space S1, S2 are the sets of vectors attributed to members of the first and second categories, respectively. Author

Subject Categories:

Distribution Statement:

APPROVED FOR PUBLIC RELEASE