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Vorlesungsverzeichnis >> Technische Fakultät (TF) >>
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Pattern Recognition (PR)- Dozent/in
- Dr.-Ing. Dieter Hahn
- Angaben
- Vorlesung
3 SWS, Schein, ECTS-Studium, ECTS-Credits: 5
geeignet als Schlüsselqualifikation
Zeit und Ort: Mo 12:00 - 14:00, H10; Mi 13:30 - 14:30, H10
ab 20.10.2010
- Studienfächer / Studienrichtungen
- WPF CE-MA 5-7
WPF INF-DH-ME 5-7
WPF INF-DH-MI 5-7
WPF IuK-DH-MMS-INF1 5-7
- ECTS-Informationen:
- Title:
- Pattern Recognition
- Credits: 5
- Contents
- This lecture gives an introduction into the basic and commonly used
classification concepts. First the necessary statistical concepts are
revised and the Bayes classifier introduced. Further concepts include
generative and discriminative models like logistic regression, the
Gaussian classifier, Linear Discriminant Analysis, the Perceptron and
Support Vector Machines (SVMs). Finally more complex methods like the
Expectation Maximization Algorithm and Hidden Markov Models are discussed.
In addition to the mentioned classifiers, methods necessary for
practical application like dimensionality reduction, optimization
methods and the use of kernel functions are explained.
In the tutorials the methods and procedures which are presented in this
lecture are illustrated using theoretical and practical exercises.
- Literature
- lecture notes
Duda R., Hart P. and Stork D.: Pattern Classification Niemann H.: Klassifikation von Mustern Niemann H.: Pattern Analysis and Understanding Fu K.S.: Sequential Methods in Pattern Recognition and Machine Learning Schürmann J.: Polynomklassifikatoren für die Zeichenerkennung
- Zusätzliche Informationen
- Schlagwörter: Mustererkennung, Vorverarbeitung, Merkmale, Klassifkation
Erwartete Teilnehmerzahl: 30
www: http://www5.informatik.uni-erlangen.de/lectures/ws-1011/pattern-recognition-pr/
- Zugeordnete Lehrveranstaltungen
- UE: Pattern Recognition Exercises
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www: http://www5.informatik.uni-erlangen.de/lectures/ws-1011/pattern-recognition-pr/
- Verwendung in folgenden UnivIS-Modulen
- Startsemester WS 2010/2011:
- Pattern Recognition (PR)
- Institution: Lehrstuhl für Informatik 5 (Mustererkennung)
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