Machine Learning: ECML 2007

Invited Talks.- Learning, Information Extraction and the Web.- Putting Things in Order: On the Fundamental Role of Ranking in Classification and Probability Estimation.- Mining Queries.- Adventures in Personalized Information Access.- Long Papers.- Statistical Debugging Using Latent Topic Models.- Learning Balls of Strings with Correction Queries.- Neighborhood-Based Local Sensitivity.- Approximating Gaussian Processes with -Matrices.- Learning Metrics Between Tree Structured Data: Application to Image Recognition.- Shrinkage Estimator for Bayesian Network Parameters.- Level Learning Set: A Novel Classifier Based on Active Contour Models.- Learning Partially Observable Markov Models from First Passage Times.- Context Sensitive Paraphrasing with a Global Unsupervised Classifier.- Dual Strategy Active Learning.- Decision Tree Instability and Active Learning.- Constraint Selection by Committee: An Ensemble Approach to Identifying Informative Constraints for Semi-supervised Clustering.- The Cost of Learning Directed Cuts.- Spectral Clustering and Embedding with Hidden Markov Models.- Probabilistic Explanation Based Learning.- Graph-Based Domain Mapping for Transfer Learning in General Games.- Learning to Classify Documents with Only a Small Positive Training Set.- Structure Learning of Probabilistic Relational Models from Incomplete Relational Data.- Stability Based Sparse LSI/PCA: Incorporating Feature Selection in LSI and PCA.- Bayesian Substructure Learning - Approximate Learning of Very Large Network Structures.- Efficient Continuous-Time Reinforcement Learning with Adaptive State Graphs.- Source Separation with Gaussian Process Models.- Discriminative Sequence Labeling by Z-Score Optimization.- Fast Optimization Methods for L1 Regularization: A Comparative Study and Two New Approaches.- Bayesian Inference for Sparse Generalized Linear Models.- Classifier Loss Under Metric Uncertainty.- Additive Groves of Regression Trees.- Efficient Computation of Recursive Principal Component Analysis for Structured Input.- Hinge Rank Loss and the Area Under the ROC Curve.- Clustering Trees with Instance Level Constraints.- On Pairwise Naive Bayes Classifiers.- Separating Precision and Mean in Dirichlet-Enhanced High-Order Markov Models.- Safe Q-Learning on Complete History Spaces.- Random k-Labelsets: An Ensemble Method for Multilabel Classification.- Seeing the Forest Through the Trees: Learning a Comprehensible Model from an Ensemble.- Avoiding Boosting Overfitting by Removing Confusing Samples.- Planning and Learning in Environments with Delayed Feedback.- Analyzing Co-training Style Algorithms.- Policy Gradient Critics.- An Improved Model Selection Heuristic for AUC.- Finding the Right Family: Parent and Child Selection for Averaged One-Dependence Estimators.- Short Papers.- Stepwise Induction of Multi-target Model Trees.- Comparing Rule Measures for Predictive Association Rules.- User Oriented Hierarchical Information Organization and Retrieval.- Learning a Classifier with Very Few Examples: Analogy Based and Knowledge Based Generation of New Examples for Character Recognition.- Weighted Kernel Regression for Predicting Changing Dependencies.- Counter-Example Generation-Based One-Class Classification.- Test-Cost Sensitive Classification Based on Conditioned Loss Functions.- Probabilistic Models for Action-Based Chinese Dependency Parsing.- Learning Directed Probabilistic Logical Models: Ordering-Search Versus Structure-Search.- A Simple Lexicographic Ranker and Probability Estimator.- On Minimizing the Position Error in Label Ranking.- On Phase Transitions in Learning Sparse Networks.- Semi-supervised Collaborative Text Classification.- Learning from Relevant Tasks Only.- An Unsupervised Learning Algorithm for Rank Aggregation.- Ensembles of Multi-Objective Decision Trees.- Kernel-Based Grouping of Histogram Data.- Active Class Selection.- Sequence Labeling with Reinforcement Learning and Ranking Algorithms.- Efficient Pairwise Classification.- Scale-Space Based Weak Regressors for Boosting.- K-Means with Large and Noisy Constraint Sets.- Towards ‘Interactive’ Active Learning in Multi-view Feature Sets for Information Extraction.- Principal Component Analysis for Large Scale Problems with Lots of Missing Values.- Transfer Learning in Reinforcement Learning Problems Through Partial Policy Recycling.- Class Noise Mitigation Through Instance Weighting.- Optimizing Feature Sets for Structured Data.- Roulette Sampling for Cost-Sensitive Learning.- Modeling Highway Traffic Volumes.- Undercomplete Blind Subspace Deconvolution Via Linear Prediction.- Learning an Outlier-Robust Kalman Filter.- Imitation Learning Using Graphical Models.- Nondeterministic Discretization of Weights Improves Accuracy of Neural Networks.- Semi-definite Manifold Alignment.- General Solution for Supervised Graph Embedding.- Multi-objective Genetic Programming for Multiple Instance Learning.- Exploiting Term, Predicate, and Feature Taxonomies in Propositionalization and Propositional Rule Learning.