STATISTICAL PATTERN RECOGNITION

A review is given of the area of statistical pattern recognition: the representation of objects and the design and evaluation of trainable systems for generalization. Traditional as well as more recently studied procedures are reviewed like the classical Bayes classifiers, neural networks, support vector machines, one-class classifiers and combining classifiers. Further we introduce methods for feature reduction and error evaluation. New developments in statistical pattern recognition are briefly discussed.