Accession Number : AD0774121

Title :   Some Aspects of Dimensionality and Sample Size Problems in Statistical Pattern Recognition,

Corporate Author : OHIO STATE UNIV COLUMBUS DEPT OF ELECTRICAL ENGINEERING

Personal Author(s) : Jain, Anil K.

Report Date : AUG 1973

Pagination or Media Count : 102

Abstract : The report is concerned with obtaining relationships between the dimensionality of a measurement vector and the number of training samples available in order to maximize the performance of a pattern classifier. In statistical pattern classification, it is known that, in general, if the class-conditional densities are not completely known and only a finite number of training samples are available, then above a certain number of measurements, the performance starts deteriorating rather than improving steadily. Previous investigators have studied conditions under which this 'curse of finite sample size' can be escaped and other properties of the optimal measurement complexity. However, all these results are valid only for the specific structural assumptions and optimal estimators and generalization to other situations had to be made heuristically. In the report, several rather general results pertaining to independent measurements and arbitrary estimators are derived. (Author)

Descriptors :   *PATTERN RECOGNITION, *STATISTICAL ANALYSIS, SAMPLING, ESTIMATES, CLASSIFICATION, THEOREMS.

Subject Categories : Cybernetics

Distribution Statement : APPROVED FOR PUBLIC RELEASE