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ORCID18th ECML 2007: Warsaw, Poland
- Joost N. Kok, Jacek Koronacki, Ramón López de Mántaras, Stan Matwin, Dunja Mladenic, Andrzej Skowron:
Machine Learning: ECML 2007, 18th European Conference on Machine Learning, Warsaw, Poland, September 17-21, 2007, Proceedings. Lecture Notes in Computer Science 4701, Springer 2007, ISBN 978-3-540-74957-8
Invited Talks (shared with PKDD 2007)
- Tom M. Mitchell:
Learning, Information Extraction and the Web. 1 - Peter A. Flach:
Putting Things in Order: On the Fundamental Role of Ranking in Classification and Probability Estimation. 2-3 - Ricardo A. Baeza-Yates:
Mining Queries. 4 - Barry Smyth:
Adventures in Personalized Information Access. 5
Long Papers
- David Andrzejewski, Anne Mulhern, Ben Liblit
, Xiaojin Zhu:
Statistical Debugging Using Latent Topic Models. 6-17 - Leonor Becerra-Bonache, Colin de la Higuera, Jean-Christophe Janodet, Frédéric Tantini:
Learning Balls of Strings with Correction Queries. 18-29 - Paul N. Bennett:
Neighborhood-Based Local Sensitivity. 30-41 - Steffen Börm, Jochen Garcke:
Approximating Gaussian Processes with H2-Matrices. 42-53 - Laurent Boyer, Amaury Habrard, Marc Sebban:
Learning Metrics Between Tree Structured Data: Application to Image Recognition. 54-66 - John Burge, Terran Lane:
Shrinkage Estimator for Bayesian Network Parameters. 67-78 - Xiongcai Cai, Arcot Sowmya:
Level Learning Set: A Novel Classifier Based on Active Contour Models. 79-90 - Jérôme Callut, Pierre Dupont:
Learning Partially Observable Markov Models from First Passage Times. 91-103 - Michael Connor, Dan Roth:
Context Sensitive Paraphrasing with a Global Unsupervised Classifier. 104-115 - Pinar Donmez, Jaime G. Carbonell, Paul N. Bennett:
Dual Strategy Active Learning. 116-127 - Kenneth Dwyer, Robert Holte:
Decision Tree Instability and Active Learning. 128-139 - Derek Greene, Padraig Cunningham:
Constraint Selection by Committee: An Ensemble Approach to Identifying Informative Constraints for Semi-supervised Clustering. 140-151 - Thomas Gärtner, Gemma C. Garriga:
The Cost of Learning Directed Cuts. 152-163 - Tony Jebara, Yingbo Song, Kapil Thadani:
Spectral Clustering and Embedding with Hidden Markov Models. 164-175 - Angelika Kimmig, Luc De Raedt, Hannu Toivonen:
Probabilistic Explanation Based Learning. 176-187 - Gregory Kuhlmann, Peter Stone:
Graph-Based Domain Mapping for Transfer Learning in General Games. 188-200 - Xiaoli Li, Bing Liu, See-Kiong Ng:
Learning to Classify Documents with Only a Small Positive Training Set. 201-213 - Xiao-Lin Li, Zhi-Hua Zhou:
Structure Learning of Probabilistic Relational Models from Incomplete Relational Data. 214-225 - Dimitrios Mavroeidis, Michalis Vazirgiannis:
Stability Based Sparse LSI/PCA: Incorporating Feature Selection in LSI and PCA. 226-237 - Andreas Nägele, Mathäus Dejori, Martin Stetter:
Bayesian Substructure Learning - Approximate Learning of Very Large Network Structures. 238-249 - Gerhard Neumann, Michael Pfeiffer, Wolfgang Maass:
Efficient Continuous-Time Reinforcement Learning with Adaptive State Graphs. 250-261 - Sunho Park, Seungjin Choi:
Source Separation with Gaussian Process Models. 262-273 - Elisa Ricci, Tijl De Bie, Nello Cristianini:
Discriminative Sequence Labeling by Z-Score Optimization. 274-285 - Mark Schmidt, Glenn Fung, Rómer Rosales:
Fast Optimization Methods for L1 Regularization: A Comparative Study and Two New Approaches. 286-297 - Matthias W. Seeger, Sebastian Gerwinn, Matthias Bethge:
Bayesian Inference for Sparse Generalized Linear Models. 298-309 - David B. Skalak, Alexandru Niculescu-Mizil, Rich Caruana:
Classifier Loss Under Metric Uncertainty. 310-322 - Daria Sorokina, Rich Caruana, Mirek Riedewald:
Additive Groves of Regression Trees. 323-334 - Alessandro Sperduti:
Efficient Computation of Recursive Principal Component Analysis for Structured Input. 335-346 - Harald Steck:
Hinge Rank Loss and the Area Under the ROC Curve. 347-358 - Jan Struyf, Saso Dzeroski:
Clustering Trees with Instance Level Constraints. 359-370 - Jan-Nikolas Sulzmann, Johannes Fürnkranz, Eyke Hüllermeier:
On Pairwise Naive Bayes Classifiers. 371-381 - Rikiya Takahashi:
Separating Precision and Mean in Dirichlet-Enhanced High-Order Markov Models. 382-393 - Stephan Timmer, Martin A. Riedmiller:
Safe Q-Learning on Complete History Spaces. 394-405 - Grigorios Tsoumakas, Ioannis P. Vlahavas:
Random k -Labelsets: An Ensemble Method for Multilabel Classification. 406-417 - Anneleen Van Assche, Hendrik Blockeel:
Seeing the Forest Through the Trees: Learning a Comprehensible Model from an Ensemble. 418-429 - Alexander Vezhnevets, Olga Barinova:
Avoiding Boosting Overfitting by Removing Confusing Samples. 430-441 - Thomas J. Walsh, Ali Nouri, Lihong Li, Michael L. Littman:
Planning and Learning in Environments with Delayed Feedback. 442-453 - Wei Wang, Zhi-Hua Zhou:
Analyzing Co-training Style Algorithms. 454-465 - Daan Wierstra, Jürgen Schmidhuber:
Policy Gradient Critics. 466-477 - Shaomin Wu, Peter A. Flach, Cèsar Ferri Ramirez:
An Improved Model Selection Heuristic for AUC. 478-489 - Fei Zheng, Geoffrey I. Webb:
Finding the Right Family: Parent and Child Selection for Averaged One-Dependence Estimators. 490-501
Short Papers
- Annalisa Appice, Saso Dzeroski:
Stepwise Induction of Multi-target Model Trees. 502-509 - Paulo J. Azevedo, Alípio Mário Jorge:
Comparing Rule Measures for Predictive Association Rules. 510-517 - Korinna Bade, Marcel Hermkes, Andreas Nürnberger:
User Oriented Hierarchical Information Organization and Retrieval. 518-526 - Sabri Bayoudh, Harold Mouchère, Laurent Miclet, Éric Anquetil:
Learning a Classifier with Very Few Examples: Analogy Based and Knowledge Based Generation of New Examples for Character Recognition. 527-534 - Steven Busuttil, Yuri Kalnishkan:
Weighted Kernel Regression for Predicting Changing Dependencies. 535-542 - András Bánhalmi, András Kocsor, Róbert Busa-Fekete:
Counter-Example Generation-Based One-Class Classification. 543-550 - Mumin Cebe, Cigdem Gunduz Demir:
Test-Cost Sensitive Classification Based on Conditioned Loss Functions. 551-558 - Xiangyu Duan, Jun Zhao, Bo Xu:
Probabilistic Models for Action-Based Chinese Dependency Parsing. 559-566 - Daan Fierens, Jan Ramon, Maurice Bruynooghe, Hendrik Blockeel:
Learning Directed Probabilistic Logical Models: Ordering-Search Versus Structure-Search. 567-574 - Peter A. Flach, Edson Takashi Matsubara:
A Simple Lexicographic Ranker and Probability Estimator. 575-582 - Eyke Hüllermeier, Johannes Fürnkranz:
On Minimizing the Position Error in Label Ranking. 583-590 - Goele Hollanders, Geert Jan Bex, Marc Gyssens, Ronald L. Westra, Karl Tuyls:
On Phase Transitions in Learning Sparse Networks. 591-599 - Rong Jin, Ming Wu, Rahul Sukthankar:
Semi-supervised Collaborative Text Classification. 600-607 - Samuel Kaski, Jaakko Peltonen:
Learning from Relevant Tasks Only. 608-615 - Alexandre Klementiev, Dan Roth, Kevin Small:
An Unsupervised Learning Algorithm for Rank Aggregation. 616-623 - Dragi Kocev, Celine Vens, Jan Struyf, Saso Dzeroski:
Ensembles of Multi-Objective Decision Trees. 624-631 - Tilman Lange, Joachim M. Buhmann:
Kernel-Based Grouping of Histogram Data. 632-639 - Rachel Lomasky, Carla E. Brodley, M. Aernecke, David R. Walt, Mark A. Friedl:
Active Class Selection. 640-647 - Francis Maes, Ludovic Denoyer, Patrick Gallinari:
Sequence Labeling with Reinforcement Learning and Ranking Algorithms. 648-657 - Sang-Hyeun Park, Johannes Fürnkranz:
Efficient Pairwise Classification. 658-665 - Jin Hyeong Park, Chandan K. Reddy:
Scale-Space Based Weak Regressors for Boosting. 666-673 - Dan Pelleg, Dorit Baras:
K -Means with Large and Noisy Constraint Sets. 674-682 - Katharina Probst, Rayid Ghani:
Towards 'Interactive' Active Learning in Multi-view Feature Sets for Information Extraction. 683-690 - Tapani Raiko, Alexander Ilin, Juha Karhunen:
Principal Component Analysis for Large Scale Problems with Lots of Missing Values. 691-698 - Jan Ramon, Kurt Driessens, Tom Croonenborghs:
Transfer Learning in Reinforcement Learning Problems Through Partial Policy Recycling. 699-707 - Umaa Rebbapragada, Carla E. Brodley:
Class Noise Mitigation Through Instance Weighting. 708-715 - Ulrich Rückert, Stefan Kramer:
Optimizing Feature Sets for Structured Data. 716-723 - Victor S. Sheng, Charles X. Ling:
Roulette Sampling for Cost-Sensitive Learning. 724-731 - Tomás Singliar, Milos Hauskrecht:
Modeling Highway Traffic Volumes. 732-739 - Zoltán Szabó, Barnabás Póczos, András Lörincz:
Undercomplete Blind Subspace Deconvolution Via Linear Prediction. 740-747 - Jo-Anne Ting, Evangelos A. Theodorou, Stefan Schaal:
Learning an Outlier-Robust Kalman Filter. 748-756 - Deepak Verma, Rajesh P. N. Rao:
Imitation Learning Using Graphical Models. 757-764 - Marcin Wojnarski:
Nondeterministic Discretization of Weights Improves Accuracy of Neural Networks. 765-772 - Liang Xiong, Fei Wang, Changshui Zhang:
Semi-definite Manifold Alignment. 773-781 - Qubo You, Nanning Zheng, Shaoyi Du, Yang Wu:
General Solution for Supervised Graph Embedding. 782-789 - Amelia Zafra, Sebastián Ventura:
Multi-objective Genetic Programming for Multiple Instance Learning. 790-797 - Monika Záková, Filip Zelezný:
Exploiting Term, Predicate, and Feature Taxonomies in Propositionalization and Propositional Rule Learning. 798-805
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