Machine Learning: ECML 2007: 18th European Conference on Machine Learning, Warsaw, Poland, September 17-21, 2007. Proceedings

Front Matter....Pages -
Learning, Information Extraction and the Web....Pages 1-1
Putting Things in Order: On the Fundamental Role of Ranking in Classification and Probability Estimation....Pages 2-3
Mining Queries....Pages 4-4
Adventures in Personalized Information Access....Pages 5-5
Statistical Debugging Using Latent Topic Models....Pages 6-17
Learning Balls of Strings with Correction Queries....Pages 18-29
Neighborhood-Based Local Sensitivity....Pages 30-41
Approximating Gaussian Processes with ${\\cal H}^2$ -Matrices....Pages 42-53
Learning Metrics Between Tree Structured Data: Application to Image Recognition....Pages 54-66
Shrinkage Estimator for Bayesian Network Parameters....Pages 67-78
Level Learning Set: A Novel Classifier Based on Active Contour Models....Pages 79-90
Learning Partially Observable Markov Models from First Passage Times....Pages 91-103
Context Sensitive Paraphrasing with a Global Unsupervised Classifier....Pages 104-115
Dual Strategy Active Learning....Pages 116-127
Decision Tree Instability and Active Learning....Pages 128-139
Constraint Selection by Committee: An Ensemble Approach to Identifying Informative Constraints for Semi-supervised Clustering....Pages 140-151
The Cost of Learning Directed Cuts....Pages 152-163
Spectral Clustering and Embedding with Hidden Markov Models....Pages 164-175
Probabilistic Explanation Based Learning....Pages 176-187
Graph-Based Domain Mapping for Transfer Learning in General Games....Pages 188-200
Learning to Classify Documents with Only a Small Positive Training Set....Pages 201-213
Structure Learning of Probabilistic Relational Models from Incomplete Relational Data....Pages 214-225
Stability Based Sparse LSI/PCA: Incorporating Feature Selection in LSI and PCA....Pages 226-237
Bayesian Substructure Learning - Approximate Learning of Very Large Network Structures....Pages 238-249
Efficient Continuous-Time Reinforcement Learning with Adaptive State Graphs....Pages 250-261
Source Separation with Gaussian Process Models....Pages 262-273
Discriminative Sequence Labeling by Z-Score Optimization....Pages 274-285
Fast Optimization Methods for L1 Regularization: A Comparative Study and Two New Approaches....Pages 286-297
Bayesian Inference for Sparse Generalized Linear Models....Pages 298-309
Classifier Loss Under Metric Uncertainty....Pages 310-322
Additive Groves of Regression Trees....Pages 323-334
Efficient Computation of Recursive Principal Component Analysis for Structured Input....Pages 335-346
Hinge Rank Loss and the Area Under the ROC Curve....Pages 347-358
Clustering Trees with Instance Level Constraints....Pages 359-370
On Pairwise Naive Bayes Classifiers....Pages 371-381
Separating Precision and Mean in Dirichlet-Enhanced High-Order Markov Models....Pages 382-393
Safe Q-Learning on Complete History Spaces....Pages 394-405
Random k -Labelsets: An Ensemble Method for Multilabel Classification....Pages 406-417
Seeing the Forest Through the Trees: Learning a Comprehensible Model from an Ensemble....Pages 418-429
Avoiding Boosting Overfitting by Removing Confusing Samples....Pages 430-441
Planning and Learning in Environments with Delayed Feedback....Pages 442-453
Analyzing Co-training Style Algorithms....Pages 454-465
Policy Gradient Critics....Pages 466-477
An Improved Model Selection Heuristic for AUC....Pages 478-489
Finding the Right Family: Parent and Child Selection for Averaged One-Dependence Estimators....Pages 490-501
Stepwise Induction of Multi-target Model Trees....Pages 502-509
Comparing Rule Measures for Predictive Association Rules....Pages 510-517
User Oriented Hierarchical Information Organization and Retrieval....Pages 518-526
Learning a Classifier with Very Few Examples: Analogy Based and Knowledge Based Generation of New Examples for Character Recognition....Pages 527-534
Weighted Kernel Regression for Predicting Changing Dependencies....Pages 535-542
Counter-Example Generation-Based One-Class Classification....Pages 543-550
Test-Cost Sensitive Classification Based on Conditioned Loss Functions....Pages 551-558
Probabilistic Models for Action-Based Chinese Dependency Parsing....Pages 559-566
Learning Directed Probabilistic Logical Models: Ordering-Search Versus Structure-Search....Pages 567-574
A Simple Lexicographic Ranker and Probability Estimator....Pages 575-582
On Minimizing the Position Error in Label Ranking....Pages 583-590
On Phase Transitions in Learning Sparse Networks....Pages 591-599
Semi-supervised Collaborative Text Classification....Pages 600-607
Learning from Relevant Tasks Only....Pages 608-615
An Unsupervised Learning Algorithm for Rank Aggregation....Pages 616-623
Ensembles of Multi-Objective Decision Trees....Pages 624-631
Kernel-Based Grouping of Histogram Data....Pages 632-639
Active Class Selection....Pages 640-647
Sequence Labeling with Reinforcement Learning and Ranking Algorithms....Pages 648-657
Efficient Pairwise Classification....Pages 658-665
Scale-Space Based Weak Regressors for Boosting....Pages 666-673
 K -Means with Large and Noisy Constraint Sets....Pages 674-682
Towards ‘Interactive’ Active Learning in Multi-view Feature Sets for Information Extraction....Pages 683-690
Principal Component Analysis for Large Scale Problems with Lots of Missing Values....Pages 691-698
Transfer Learning in Reinforcement Learning Problems Through Partial Policy Recycling....Pages 699-707
Class Noise Mitigation Through Instance Weighting....Pages 708-715
Optimizing Feature Sets for Structured Data....Pages 716-723
Roulette Sampling for Cost-Sensitive Learning....Pages 724-731
Modeling Highway Traffic Volumes....Pages 732-739
Undercomplete Blind Subspace Deconvolution Via Linear Prediction....Pages 740-747
Learning an Outlier-Robust Kalman Filter....Pages 748-756
Imitation Learning Using Graphical Models....Pages 757-764
Nondeterministic Discretization of Weights Improves Accuracy of Neural Networks....Pages 765-772
Semi-definite Manifold Alignment....Pages 773-781
General Solution for Supervised Graph Embedding....Pages 782-789
Multi-objective Genetic Programming for Multiple Instance Learning....Pages 790-797
Exploiting Term, Predicate, and Feature Taxonomies in Propositionalization and Propositional Rule Learning....Pages 798-805
Back Matter....Pages -