Pattern recognition methods


Aim of the course

The aim of the course is to gain the ability to create systems used for detection, identification and authorisiation and for building data-based models using the methodology and algorithms for pattern recognition.

Lecture programme

The properties space. Generation of properties. Curse of dimension – PAC theory and Vapnik’s dimension. Analysis of the signal and image processing. Transform (Fourier, wavelet, Hough, Radon, fractal, etc.). Measures of similarity and dissimilarity. Classifiers vs. Interpolation and approximation. Simple classifiers. Linear Classifiers (preceptron, Fischer). Bayesian Classifiers. Nonlinear-team classifiers. Neural Networks. Support Vector Machine. Ada Boost and other boosting methods. Visualization of multidimensional information. Linear Methods: principal components method (principal component analysis) and the method of linear discrimination (linear discriminant analysis). Nonlinear Methods: multidimensional scaling MDS (Multidimensional Scaling). Measures of mapping fidelity. Clustering methods. Hierarchical and nonhierarchical clustering. Modern methods of clustering: SNN, DBSCAN, CHAMELEON. Validation of clustering methods. Complex networks and their use in the methods of pattern recognition.

Overview of the course elements

The laboratory classes will be conducted using the basic toolboxes of Matlab. Students carry out tasks related to the systems that recognize major components: generation of properties (use transforms), classification (the use and comparison of linear and nonlinear methods), multivariate information visualization (PCA and MDS), clustering. Labs will deal with the analysis of the data in the available data repositories on the Internet as well those supplied by the person who leads the classes. The activity of students during classes and a report prepared on time contribute to grading.

Reading list

1. Theodoris S and Koutroumbas K, Pattern Recognition, Academic Press, San Diego, London, Boston, 1998.
2. Strang, G. and Nguyen, T., Wavelets and Filter Banks, Wellesley-Cambridge Press, Wellesley, MA, 1996.
3. Mitra, S. and Acharya T., Data Mining: Multimedia, Soft Computing and Bioinformatics, 424pp. J. Wiley, 2003.
4. Grossman R., L., Karnath, Ch, Kegelmeyer, P., Kumar, V., Namburu, R.,R., Data Mining for Scientific and Engineering Applications, Kluwer Academic Publisher, 2001
5. R.Duda, P.Hart, D. Stork (Patterns Classification)

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