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Inductive logic programming - techniques and applications
We use attribute-value learners in an ILP framework to learn relations from noisy examples. Expand
An extensive experimental comparison of methods for multi-label learning
We present an extensive experimental comparison of 12 multi-label learning methods using 16 evaluation measures over 11 benchmark datasets. Expand
Decision trees for hierarchical multi-label classification
Hierarchical multi-label classification (HMC) is a variant of classification where instances may belong to multiple classes at the same time and these classes are organized in a hierarchy. Expand
A large-scale evaluation of computational protein function prediction
Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. IfExpand
Is Combining Classifiers with Stacking Better than Selecting the Best One?
We empirically evaluate several state-of-the-art methods for constructing ensembles of heterogeneous classifiers with stacking and show that they perform (at best) comparably to selecting the best classifier from the ensemble by cross validation. Expand
Learning model trees from evolving data streams
This paper proposes an efficient and incremental stream mining algorithm which is able to learn regression and model trees from possibly unbounded, high-speed and time-changing data streams. Expand
Multi-relational data mining: an introduction
Multi-relational data mining (MRDM) approaches look for patterns that involve multiple tables (relations) from a relational database. Expand
Towards a Slovene Dependency Treebank
The paper presents the initial release of the Slovene Dependency Treebank, currently containing 2000 sentences or 30.000 words. Expand
Hierarchical annotation of medical images
We present a hierarchical multi-label classification (HMC) system for medical image annotation. Expand
Proceedings of the 22nd international conference on Machine learning
This volume, which is also available online from http://www.machinelearning.org, contains the papers accepted for presentation at ICML-2005, the 22nd lnternational Conference on Machine Learning, which was held at the University of Bonn in Germany from August 7 to August 11, 2005. Expand