Pattern Recognition Prof. Rolf-Rainer Grigat
Main Study, 2 SWS Lecture, Summer Semester, Implementation in English
Participating students can download the learning material and exercises from Stud.IP.
There is also a forum on the Stud.IP platform.
The lecture treats fundamental methods of the pattern recognition.
Sample applications are the recognition of material defects, contents-based accesses to data bases, the analysis of machine writing and handwriting or the analysis of lip language
- Basics of the Pattern Recognition
- MAP Classification, Bayes Classificator, Maximum Likelihood
- Polynomial Classification (Reduction of the Dimension, Confidence Illustration, Recursive Training)
- Multilevel Perceptrons (Training by Means of Gradient Descent Procedures)
- Radially basis Functions, Cluster Analysis