Pattern recognition techniques: Studies on appropriate classifications

Abstract

Pattern recognition techniques are divided into categories of supervised, unsupervised and semi supervised. Supervised pattern recognition methods are utilized in the examination of various sources' chemical data such as sensor measurements, spectroscopy, and chromatography. The unsupervised classification techniques use algorithms to classify and analyze huge amounts of raster cells. Semi-Supervised Learning is an approach that is in the middle ground between supervised and unsupervised learning and guarantees to be better at classification by involving data that is unlabeled. In this paper, we tried to categories pattern recognition methods and explain about each of them and we compared supervised method with unsupervised method in terms of types and location of features. INTRODUCTION Pattern recognition techniques are divided into categories of supervised, unsupervised and semi supervised. This is dependent on the analyst's intention of the information that needs to be utilized or that is available regarding the samples comprising of the data matrix. In the supervised methods, or the classification method, prior description is made on the classes as the concept or the attribute used to classify the samples into subsets are already known [1]. In the unsupervised method, the classification is removed by considering only the variations and resemblances among the samples, without utilizing any of their details. The semi-supervised method is in the middle ground between the supervised and unsupervised analysis and assures to be a better classification using the non-labeled details.