Machine Learning and Knowledge Discovery in Databases: ECML

from Semi-structured Web Pages.- Online Multiagent Learning against Memory Bounded Adversaries.- Scalable Feature Selection for Multi-class Problems.- Learning Decision Trees for Unbalanced Data.- Credal Model Averaging: An Extension of Bayesian Model Averaging to Imprecise Probabilities.- A Fast Method for Training Linear SVM in the Primal.- On the Equivalence of the SMO and MDM Algorithms for SVM Training.- Nearest Neighbour Classification with Monotonicity Constraints.- Modeling Transfer Relationships Between Learning Tasks for Improved Inductive Transfer.- Mining Edge-Weighted Call Graphs to Localise Software Bugs.- Hierarchical Distance-Based Conceptual Clustering.- Mining Frequent Connected Subgraphs Reducing the Number of Candidates.- Unsupervised Riemannian Clustering of Probability Density Functions.- Online Manifold Regularization: A New Learning Setting and Empirical Study.- A Fast Algorithm to Find Overlapping Communities in Networks.- A Case Study in Sequential Pattern Mining for IT-Operational Risk.- Tight Optimistic Estimates for Fast Subgroup Discovery.- Watch, Listen & Learn: Co-training on Captioned Images and Videos.- Parameter Learning in Probabilistic Databases: A Least Squares Approach.- Improving k-Nearest Neighbour Classification with Distance Functions Based on Receiver Operating Characteristics.- One-Class Classification by Combining Density and Class Probability Estimation.- Efficient Frequent Connected Subgraph Mining in Graphs of Bounded Treewidth.- Proper Model Selection with Significance Test.- A Projection-Based Framework for Classifier Performance Evaluation.- Distortion-Free Nonlinear Dimensionality Reduction.- Learning with L q? vs L 1-Norm Regularisation with Exponentially Many Irrelevant Features.- Catenary Support Vector Machines.- Exact and Approximate Inference for Annotating Graphs with Structural SVMs.- Extracting Semantic Networks from Text Via Relational Clustering.- Ranking the Uniformity of Interval Pairs.- Multiagent Reinforcement Learning for Urban Traffic Control Using Coordination Graphs.- StreamKrimp: Detecting Change in Data Streams.