Machine Learning and Knowledge Discovery in Databases

Regular Papers.- Exceptional Model Mining.- A Joint Topic and Perspective Model for Ideological Discourse.- Effective Pruning Techniques for Mining Quasi-Cliques.- Efficient Pairwise Multilabel Classification for Large-Scale Problems in the Legal Domain.- Fitted Natural Actor-Critic: A New Algorithm for Continuous State-Action MDPs.- A New Natural Policy Gradient by Stationary Distribution Metric.- Towards Machine Learning of Grammars and Compilers of Programming Languages.- Improving Classification with Pairwise Constraints: A Margin-Based Approach.- Metric Learning: A Support Vector Approach.- Support Vector Machines, Data Reduction, and Approximate Kernel Matrices.- Mixed Bregman Clustering with Approximation Guarantees.- Hierarchical, Parameter-Free Community Discovery.- A Genetic Algorithm for Text Classification Rule Induction.- Nonstationary Gaussian Process Regression Using Point Estimates of Local Smoothness.- Kernel-Based Inductive Transfer.- State-Dependent Exploration for Policy Gradient Methods.- Client-Friendly Classification over Random Hyperplane Hashes.- Large-Scale Clustering through Functional Embedding.- Clustering Distributed Sensor Data Streams.- A Novel Scalable and Data Efficient Feature Subset Selection Algorithm.- Robust Feature Selection Using Ensemble Feature Selection Techniques.- Effective Visualization of Information Diffusion Process over Complex Networks.- Actively Transfer Domain Knowledge.- A Unified View of Matrix Factorization Models.- Parallel Spectral Clustering.- Classification of Multi-labeled Data: A Generative Approach.- Pool-Based Agnostic Experiment Design in Linear Regression.- Distribution-Free Learning of Bayesian Network Structure.- Assessing Nonlinear Granger Causality from Multivariate Time Series.- Clustering Via Local Regression.- Decomposable Families of Itemsets.- Transferring Instances for Model-Based Reinforcement Learning.- A Simple Model for Sequences of Relational State Descriptions.- Semi-Supervised