Selected Topics in Pattern Recognition

Course guarantor:

Lecturer:

Tutor:

Supervisor:

Department of Cybernetics

Synopsis:

Prerequisites: basic course in pattern recognition (e.g. P33ROD, 33RPZ).

Selected topics: Anderson's problem, Kozince algorithm, kernel perceptron,

nonlinear Fisher discriminant. Vapnik's learning theorz. Deterministic

learning. Unsupervised learning: Robbins algorithm and emprirical Bayesian

approach. Expectation-minimization algorithm. Recognition of sequences and

directed acyclic graphs. Markov models. Combination of weak classifiers:

boosting and bagging. AdaBoost.

Requirements:

Syllabus of lectures:

Syllabus of tutorials:

Study Objective:

Study materials:

Duda, Richard O.; Hart, Peter E.; Stork, David G.: Pattern Classification,

John Wiley, 2001

Note:

Further information:

No time-table has been prepared for this course

The course is a part of the following study plans:

• Doctoral studies, daily studies (compulsory elective course)
• Doctoral studies, combined studies (compulsory elective course)
• Doctoral studies, structured daily studies (compulsory elective course)
• Doctoral studies, structured combined studies (compulsory elective course)