Accession Number : AD0730285

Title :   Generative, Descriptive, Heuristic, and Formal Modeling in Pattern Analysis and Classification.

Descriptive Note : Technical rept. 3 Apr 69-4 May 70,

Corporate Author : PHILCO-FORD CORP WILLOW GROVE PA COMMUNICATIONS AND TECHNICAL SERVICES DIV

Personal Author(s) : Kanal,Laveen N.

Report Date : JUL 1971

Pagination or Media Count : 112

Abstract : Generative grammars and pattern descriptions have been recently associated primarily with the Linguistic School of pattern recognition research. The author shows that very similar types of generative and descriptive models are used extensively in obtaining stochastic models for patterns. The pure linguistic model is usually presented as a contrast to the pure statistical classification model. Neither pure model is relevant to applications. Rather it is hybrid linguistic-statistical approaches that have been useful in practice. The author examines the relationship of measurement analysis to pattern classification, formalisms for pattern analysis and grammatical inference, heurristic transformations for inferring pattern grammars, and the role which heuristics play in making a formalism successful. The author presents an example of the generative and descriptive modeling of error-clusters and error-gaps which occur in the transmission of binary data over digital communication channels with memory and conclude with a discussion of the testing of theories and models. (Author)

Descriptors :   (*PATTERN RECOGNITION, *GRAMMARS), (*COMPILERS, PATTERN RECOGNITION), SYNTAX, STATISTICAL ANALYSIS, LEARNING MACHINES, MATHEMATICAL LOGIC, SET THEORY, STOCHASTIC PROCESSES, ARTIFICIAL INTELLIGENCE, MATHEMATICAL MODELS, DECISION THEORY

Subject Categories : Linguistics
      Computer Programming and Software
      Bionics

Distribution Statement : APPROVED FOR PUBLIC RELEASE