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Last update: TAJ445 (14.12.2013)
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Last update: Kukal Jaromír doc. Ing. Ph.D. (11.09.2013)
Students will be able to: Design descriptors for statistical pattern recognition Apply data transforms for improvement of pattern recognition quality Apply standard pattern recognition methods Evaluate quality of pattern recognition systém |
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Last update: TAJ445 (30.09.2013)
R:Fukunaga K., Introduction to Statistical Pattern Recognition, Academic Press, London, 1990 R:Shawe-Taylor J., Cristianini N., Kernel Methods for Pattern Analysis, Cambridge University Press, Cambridge, 2009 A:Scholkopf B., Smola A.J., Learning with Kernels, MIT Press, Cambridge, 2002 |
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Last update: Kukal Jaromír doc. Ing. Ph.D. (11.09.2013)
interal training materials |
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Last update: Kukal Jaromír doc. Ing. Ph.D. (11.09.2013)
1. Class, pattern, descriptors, pattern set for statistical pattern recognition 2. Repetitorium of basic statistical terms and principles 3. Quality of desroptor set: the best descriptor, acceptable subset of descriptors 4. Linear discrimination analysis as a tool for pattern recognition 5. Evaluation of pattern recognition quality: p-value, sensitivity, specificity, error, AIC, BIC 6. Cross-validation methodology in evaluation of recognition quality 7. Linear data transforms: normaliztion, standardization, PCA, spherization 8. Robust and regularized methods and their advantages in pattern recognition 9. Application of metrics in pattern recognition: Euclidean, Minkowski, Mahalanobis, k-NN, c-mean 10. Application of PDF in pattern recognition: GMM, Parzen and LQ estimates 11. Linear, non-linear, and logistic regression as pattern recognition tools 12. Feature space reduction as application of binary optimization 13. Kernel functions in design of non-linear classifiers 14. Fuzzy sets in pattern recognition: fuzzification, FCM |
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Last update: Kukal Jaromír doc. Ing. Ph.D. (11.09.2013)
Basic knowledge of mathematical statistics and programming in MATLAB. |
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Last update: TAJ445 (30.09.2013)
none |
Teaching methods | ||||
Activity | Credits | Hours | ||
Konzultace s vyučujícími | 0.5 | 14 | ||
Účast na přednáškách | 1 | 28 | ||
Práce na individuálním projektu | 2 | 56 | ||
Příprava na zkoušku a její absolvování | 0.5 | 14 | ||
Účast na seminářích | 1 | 28 | ||
5 / 5 | 140 / 140 |
Coursework assessment | |
Form | Significance |
Regular attendance | 40 |
Oral examination | 60 |