GfKl 2008 - Legos
GfKl 2008 - Legos
GfKl 2008 - Legos
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Session Market Risk and Credit<br />
Risk<br />
11:45-<br />
12:10<br />
12:10-<br />
12:35<br />
12:35-<br />
13:00<br />
13:00-<br />
14:00<br />
14:00-<br />
14:45<br />
Bravo, Cristian;<br />
Maldonado, Sebastian;<br />
Weber, Richard<br />
(Chair: Locarek-Junge) Room<br />
4<br />
Practical experiences from Credit<br />
Scoring projects for Chilean<br />
financial organizations<br />
21<br />
Kuziak, Katarzyna An application of copula functions<br />
to market risk management<br />
Rokita, Pawel; Piontek,<br />
Krzysztof<br />
Lunch (and Meetings)<br />
Extreme unconditional<br />
dependence vs. multivariate<br />
GARCH effect in the analysis of<br />
dependence between high losses<br />
on Polish and German stock<br />
indexes<br />
Plenary Lecture (Chair: Lausen)<br />
Schölkopf, Bernhard Machine Learning applications of<br />
positive definite kernels<br />
Session Mixture Analysis II:<br />
Clustering and<br />
Classification<br />
14:50-<br />
15:15<br />
15:15-<br />
15:40<br />
15:40-<br />
16:05<br />
16:05-<br />
16:30<br />
Pons, Odile Classification with an increasing<br />
number of components<br />
Lukociene, Olga;<br />
Vermunt, Jeroen K.<br />
Calò, Daniela G.; Viroli,<br />
Cinzia<br />
Latouche, Pierre J.;<br />
Ambroise, Christophe;<br />
Birmelé, Etienne<br />
Session Pattern Recognition and<br />
Machine Learning II<br />
14:50-<br />
15:15<br />
15:15-<br />
15:40<br />
15:40-<br />
16:05<br />
Stecking, Ralf;<br />
Schebesch, Klaus B.<br />
Huellermeier, Eyke;<br />
Vanderlooy, Stijn<br />
Hühn, Jens;<br />
Hüllermeier, Eyke<br />
83<br />
121<br />
132 Room<br />
5<br />
(Chair: Montanari) Room<br />
3<br />
Determining the number of<br />
components in mixture models for<br />
hierarchical data<br />
Visualizing data in Gaussian<br />
mixture model classification<br />
Bayesian Methods for Graph<br />
Clustering<br />
114<br />
90<br />
24<br />
85<br />
(Chair: Nalbantov) Room<br />
405/6<br />
Generating Fictitious Training Data<br />
for Credit Client Classification<br />
Combining Predictions in Pairwise<br />
Classification: An Adaptive Voting<br />
Strategy and Its Relation to<br />
Weighted Voting<br />
Rule-Based Learning of Reliable<br />
Classifiers<br />
− xiv −<br />
140<br />
70<br />
69
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