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This lecture is focused on principal concepts of pattern recognition. The contents of the lecture is the description and analysis of various methods applicable to pattern recognition.
Last update: T_KSI (05.05.2004)
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Richard O. Duda, Peter E. Hart, David G. Stork - Pattern Classification, Second Edition, A Wiley-Interscience Publication, 2000 Sergios Theodoridis, Konstantinos Koutroumbas - Pattern Recognition, Second Edition, Elsevier Academic Press, 2003 Evangelia Micheli-Tzanakou - Supervised and Unsupervisd Pattern Recognition, Feature Extraction and Computational Intellingence, CRC Press, 2000 Last update: T_KSI (06.05.2004)
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Introduction to pattern recognition: Features, feature vectors, classifiers. Pattern recognition systems, the design cycle, learning and adaptation. Supervised versus unsupervised pattern recognition.
Supervised pattern recogniton: Classifiers based on Bayes decision theory. The nearest-neighbor rule. Linear and nonlinear classifiers - the perception algorithm, support vector machine, the multilayer perceptions, stochastic methods. Decisions trees. Recognition with strings. Template matching.
Unsupervised pattern recognition: Basic concept of clustering. Proximity measures. Criterion functions for clustering. Iterative optimization. Sequential clustering. Hierarchical clustering. Clustering based on graph theory. Competitive learning algorithms. Clustering via cost optimization. Cluster validity. Last update: Štanclová Jana, RNDr., Ph.D. (12.12.2013)
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