Machine Learning and Knowledge Discovery in Databases European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010. Proceedings, Part II

Regular Papers.- Bayesian Knowledge Corroboration with Logical Rules and User Feedback.- Learning an Affine Transformation for Non-linear Dimensionality Reduction.- NDPMine: Efficiently Mining Discriminative Numerical Features for Pattern-Based Classification.- Hidden Conditional Ordinal Random Fields for Sequence Classification.- A Unifying View of Multiple Kernel Learning.- Evolutionary Dynamics of Regret Minimization.- Recognition of Instrument Timbres in Real Polytimbral Audio Recordings.- Finding Critical Nodes for Inhibiting Diffusion of Complex Contagions in Social Networks.- Semi-supervised Abstraction-Augmented String Kernel for Multi-level Bio-Relation Extraction.- Online Knowledge-Based Support Vector Machines.- Learning with Randomized Majority Votes.- Exploration in Relational Worlds.- Efficient Confident Search in Large Review Corpora.- Learning to Tag from Open Vocabulary Labels.- A Robustness Measure of Association Rules.- Automatic Model Adaptation for Complex Structured Domains.- Collective Traffic Forecasting.- On Detecting Clustered Anomalies Using SCiForest.- Constrained Parameter Estimation for Semi-supervised Learning: The Case of the Nearest Mean Classifier.- Online Learning in Adversarial Lipschitz Environments.- Summarising Data by Clustering Items.- Classification and Novel Class Detection of Data Streams in a Dynamic Feature Space.- Latent Structure Pattern Mining.- First-Order Bayes-Ball.- Learning from Demonstration Using MDP Induced Metrics.- Demand-Driven Tag Recommendation.- Solving Structured Sparsity Regularization with Proximal Methods.- Exploiting Causal Independence in Markov Logic Networks: Combining Undirected and Directed Models.- Improved MinMax Cut Graph Clustering with Nonnegative Relaxation.- Integrating Constraint Programming and Itemset Mining.- Topic Modeling for Personalized Recommendation of Volatile Items.- Conditional Ranking on Relational Data.