Accession Number : AD0747706

Title :   Quantization of Independent Measurements and Recognition Performance,

Corporate Author : OHIO STATE UNIV COLUMBUS COMPUTER AND INFORMATION SCIENCE RESEARCH CENTER

Personal Author(s) : Chandrasekaran,B. ; Jain,A. K.

Report Date : MAR 1972

Pagination or Media Count : 16

Abstract : It is known that for the pattern classification problem where only a finite number of training samples are available, in general performance improves, reaches a maximum, and then starts deteriorating as the number of measurements is increased. However, one of the authors has shown that for independent measurements of binary quantization, the measurement complexity can be arbitrarily increased without fear of this peaking of performance. In the paper the authors consider the case of independent measurements with arbitrary quantization. (Author)

Descriptors :   (*INFORMATION THEORY, *PATTERN RECOGNITION), DECISION THEORY, PROBABILITY, DENSITY, FUNCTIONS(MATHEMATICS), CLASSIFICATION, STATISTICAL ANALYSIS

Subject Categories : Cybernetics

Distribution Statement : APPROVED FOR PUBLIC RELEASE