Titlebook: Machine Learning and Knowledge Discovery in Databases, Part III; European Conference, Dimitrios Gunopulos,Thomas Hofmann,Michalis Vazirg Co

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Compact Coding for Hyperplane Classifiers in Heterogeneous Environmentwpoint to make the choice of the specific source task more accurate. Extensive experiments show the effectiveness of our algorithm in terms of the classification accuracy in both UCI and text data sets.

Externalize

Multi-label Ensemble Learninged EnML, to effectively augment the accuracy as well as the diversity of multi-label base learners. In detail, we design two objective functions to evaluate the accuracy and diversity of multi-label base learners, respectively, and EnML simultaneously optimizes these two objectives with an evolution

HPA533

Rule-Based Active Sampling for Learning to Rankions, our algorithm does not rely on an initial training seed and can be directly applied to an unlabeled dataset. Also in contrast to previous work, we have a clear stop criterion and do not need to empirically discover the best configuration by running a number of iterations on the validation or t

Diastole

Parallel Structural Graph Clusteringvious work. For harder parameter settings, it was possible to obtain results within reasonable time for 300,000 structures, compared to 10,000 structures in previous work. This shows that structural, scaffold-based clustering of smaller libraries for virtual screening is already feasible.

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