6. MLDM 2009 Posters:
Leipzig,
Germany
Petra Perner (Ed.):
Machine Learning and Data Mining in Pattern Recognition, 6th International Conference, MLDM 2009, Leipzig, Germany, July 2009, Poster Proceedings.
IBaI Publishing 2009, ISBN 978-3-940501-04-2 
Aspects of Data Mining
Data Mining on Multimedia Data
- Liang-Hua Chen, Chih-Wen Su, Chi-Feng Weng, Hong-Yuan Mark Liao:
SVM Based Action Scene Detection.
45-50
- Michelle Karg, Robert Jenke, Kolja Kühnlenz, Martin Buss:
A Two-fold PCA-Approach for Inter-Individual Recognition of Emotions in Natural Walking.
51-61
- Sara Colantonio, Igor B. Gurevich, Ovidio Salvetti, Yulia Trusova:
A Semantic Framework for Mining Novel Knowledge from Multimedia Data.
62-67
- Joona Laukkanen, Georgi Stojanov, Vesna Poprcova:
Some Improvements of the ILP System HYPER Used in the Context of Robotic Learning.
68-73
Theory
- Georgy Shevlyakov, Maya Shevlyakova, Vladimir Shin:
Robust Supervised Classifiers for Nondegenerate Generalized Gaussian Pattern Distributions with a Bounded Variance.
74-78
- Asem Eltaher, Markus Maurer, Thomas Form, Mohamed Ayeb:
How much Testing is Enough? A Learning Approach.
79-93
- Alexander Adam, Sebastian Leuoth, Wolfgang Benn:
Performance Gain of Different Parallelization Approaches for Growing Neural Gas.
94-103
- Zhong-Liang Fu, Yu Yao, Xiang-Hui Zhao:
The Best Combining of Classifiers with Prior Probabilities.
104-114
- Tim vor der Brück:
Approximation of the Parameters of a Readability Formula by Robust Regression.
115-125
- François Bouchet, Jean-Paul Sansonnet:
Tree Kernel and Feature Vector Methods for Formal Semantic Requests Classification.
126-140
- Sebastian Leuoth, Wolfgang Benn:
Towards SISI - a Self Adaptive Insert Strategy for the Intelligent Cluster Index (ICIx).
141-155
- Liyun Chu, W. K. Wong, S. Y. S. Leung:
Optimization of the Weighting Exponent in the Fuzzy c-means Clustering.
156-160
Distance and Similarity
- Sergey Dvoenko:
Clustering and Separating of a Set of Members in Terms of Mutual Distances and Proximities.
161-175
Oral Paper Late
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