Pattern recognition and classification

Abstract: Pattern recognition is about assigning objects (also called observations, instances or examples) to classes. The objects are described by features and represented as points in the feature space. A classifier is an algorithm that assigns a class label to any given point in the feature space. Pattern recognition comprises supervised learning (predefined class labels) and unsupervised learning (unknown class labels). Supervised learning includes choosing a classifier model, training and testing the classifier and selecting relevant features. Classifier ensembles combine the outputs of a set of classifiers for improved accuracy. Unsupervised learning is usually approached by cluster analysis.