Pattern Recognition (USK)

Annotation

Basic concepts of pattern recognition and learning classifiers. Bayes' classifier, decision function. Classifiers for separable and inseparable classes. Loss function and stochastic approximation. Linear discriminant function, learning algorithms. Unsupervised learning: cluster analysis. Hierarchical and nonhierarchical approaches, simple cluster-seeking algorithms, k-means algorithms, ISODATA algorithms, etc. Feature extraction and selection. Examples of pattern recognition systems.