Seminars/Colloquia/Invited Talks

Seminars

Robert P.W. Duin

The dissimilarity representation for pattern classification

PLACE:	Clark 110
EVENT:	CIS Seminar
DATE:	March 4, 2008
TIME:	1:00 - 2:00 PM

Abstract

Traditionally features are used for building a representation that is useful for statistical pattern recognition. Other vector representations may be considered as well in case it is difficult to define such features from the available background knowledge of the application. A review will be given of the possibilities of using dissimilarities. We will focus on defining vector spaces in which traditional classifiers like LDA, neural networks and SVM may be trained for data like images, spectra, sequences and graphs.

Brief Biography:

Dr.Robert P.W. Duin studied applied physics at Delft University of Technology in the Netherlands. In 1978 he received the Ph.D. degree for a thesis on the accuracy of statistical pattern recognizers. In his research he included various aspects of the automatic interpretation of measurements, learning systems and classifiers. Between 1980 and 1990 he studied and developed hardware architectures and software configurations for interactive image analysis. After this period his interest was redirected via neural networks to pattern recognition. At this moment he is an associate professor of the Faculty of Electrical Engineering, Mathematics and Computer Science of Delft University of Technology. His present research is in the design, evaluation and application of algorithms that learn from examples. This includes neural network classifiers, support vector machines, classifier combining strategies and one-class classifiers. Especially complexity issues and the learning behavior of trainable systems receives much interest. Recently he started to investigate alternative object representations for classification and he became thereby interested in dissimilarity based pattern recognition, trainable similarities and the handling of non-Euclidean data. He expects that this will contribute to the unification of learning from structure and learning from statistics. The pattern recognition research team headed by Robert Duin studies many industrial and medical applications. They are thereby interested in pattern recognition system design, the handling of ill-sampled problems and in varying costs and prior probabilities. A series of pattern recognition courses for industry has been set up by this team. The software (PRTools) is public available for research purposes. Robert Duin is a former associate editor of IEEE Transactions on Pattern Analysis and Machine Intelligence. Presently he is an advisory editor of Pattern Recognition Letters. He is a member of the IEEE, and a fellow of the IAPR. In August 2006 he received the Pierre Devijver Award for his contributions to statistical pattern recognition.