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Course, academic year 2017/2018
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Pattern Recognition - NAIL072
Czech title: Rozpoznávání vzorů
Guaranteed by: Department of Software Engineering (32-KSI)
Faculty: Faculty of Mathematics and Physics
Actual: from 2015
Semester: summer
E-Credits: 3
Hours per week, examination: summer s.:2/0 Ex [hours/week]
Capacity: unlimited
Min. number of students: unlimited
State of the course: cancelled
Language: Czech
Teaching methods: full-time
Guarantor: RNDr. Jana Štanclová, Ph.D.
Class: DS, softwarové systémy
Informatika Mgr. - Matematická lingvistika
Classification: Informatics > Theoretical Computer Science
Annotation -
Last update: T_KSI (05.05.2004)

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.
Literature - Czech
Last update: T_KSI (06.05.2004)

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

Syllabus -
Last update: RNDr. Jana Štanclová, Ph.D. (12.12.2013)

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.

 
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