Fundamentals of Pattern Recognition
Ludmila Ilieva Kuncheva · 2014
This introductory chapter talks about fundamentals of pattern recognition. It explains basic concepts including class, feature and dataset involved in pattern recognition. Depending on the classifier model, the ordering of the categories and the scaling of the values may have a positive, negative, or neutral effect on the relevance of the feature. The performance of a classifier is a compound characteristic, whose most important component is the classification accuracy. Pattern recognition developed historically as a union of three distinct but intrinsically related components: classification, clustering, and feature selection. Although many types of uncertainty exist, the probabilistic model fits surprisingly well in most pattern recognition problems. Using the true posterior probabilities or an equivalent set of discriminant functions guarantees the smallest possible error rate, called the Bayes error. The chapter concludes with the explanation of real-life data pose challenges such as unbalanced classes, uncertain labels, massive volumes and nonstationary distributions.