Accession Number:

AD0843982

Title:

Learning in Pattern Recognition.

Descriptive Note:

Technical rept.,

Corporate Author:

STANFORD UNIV CA STANFORD ELECTRONICS LABS

Personal Author(s):

• Cover, Thomas M.

Report Date:

1968-08-01

Pagination or Media Count:

45.0

Abstract:

This paper is specifically concerned with the problem of inferring from a finite set of patterns the classification of an unknown pattern. A discussion of the general problems inherent in the concept of learning and data reduction are discussed from a standpoint of measurement selection for the general pattern recognition problem. A brief history of the existent work in empirical Bayes and compound sequential Bayes procedures will be presented. It is felt that these procedures are basically non-Bayesian, despite their names, and are therefore especially suited to problems arising in pattern recognition. Finally, a discussion is made of some nonparametric approaches to the problem of the classification of an unknown pattern when the only information on the underlying distributions associated with the various categories is that which can be obtained from a finite number of samples. Author

Descriptors:

• *PATTERN RECOGNITION)
• (*LEARNING
• ALGORITHMS
• DECISION MAKING
• MEMORY(PSYCHOLOGY)
• REDUCTION
• PROBABILITY
• STATISTICAL PROCESSES
• SET THEORY
• ERRORS

Subject Categories:

• Psychology

Distribution Statement:

APPROVED FOR PUBLIC RELEASE