STATISTICAL AND RELATED APPROACHES TO PATTERN RECOGNITION

The task of Pattern Recognition occurs in a wide range of human activities. The term could cover any context in which some decision or forecast is made on the basis of currently available information and a Pattern Recognition is then some formal method for repeatedly making such judgments in new situations. We shall assume that the problem concerns construction a procedure that will be applied to a continuing sequence of cases, in which each new case must be assigned to one of a set of predefined classes on the basis of observed attributes or features. The construction of classification procedure from a set of data for which the true classes are known has also been variously termed Pattern Recognition, discrimination or supervised learning. A modest attempt is made to review the following approaches to Pattern Recognition: Statistical, Neural, Fuzzy and Syntactic. The review presented here is not exhaustive, because of its interdisciplinary nature. This work is useful to the budding researchers to work in this area to familiarize themselves about pattern Recognition.