Machine Learning: ECML 2007

1. Front Matter

   PDF

2. Invited Talks

   1. Learning, Information Extraction and the Web

      • Tom M. Mitchell

      Pages 1-1

   2. Putting Things in Order: On the Fundamental Role of Ranking in Classification and Probability Estimation

      • Peter A. Flach

      Pages 2-3

   3. Mining Queries

      • Ricardo Baeza-Yates

      Pages 4-4

   4. Adventures in Personalized Information Access

      • Barry Smyth

      Pages 5-5

3. Long Papers

   1. Statistical Debugging Using Latent Topic Models

      • David Andrzejewski, Anne Mulhern, Ben Liblit, Xiaojin Zhu

      Pages 6-17

   2. Learning Balls of Strings with Correction Queries

      • Leonor Becerra Bonache, Colin de la Higuera, Jean-Christophe Janodet, Frédéric Tantini

      Pages 18-29

   3. Neighborhood-Based Local Sensitivity

      • Paul N. Bennett

      Pages 30-41

   4. Approximating Gaussian Processes with \({\cal H}^2\)-Matrices

      • Steffen Börm, Jochen Garcke

      Pages 42-53

   5. Learning Metrics Between Tree Structured Data: Application to Image Recognition

      • Laurent Boyer, Amaury Habrard, Marc Sebban

      Pages 54-66

   6. Shrinkage Estimator for Bayesian Network Parameters

      • John Burge, Terran Lane

      Pages 67-78

   7. Level Learning Set: A Novel Classifier Based on Active Contour Models

      • Xiongcai Cai, Arcot Sowmya

      Pages 79-90

   8. Learning Partially Observable Markov Models from First Passage Times

      • Jérôme Callut, Pierre Dupont

      Pages 91-103

   9. Context Sensitive Paraphrasing with a Global Unsupervised Classifier

      • Michael Connor, Dan Roth

      Pages 104-115

   10. Dual Strategy Active Learning

       • Pinar Donmez, Jaime G. Carbonell, Paul N. Bennett

       Pages 116-127

   11. Decision Tree Instability and Active Learning

       • Kenneth Dwyer, Robert Holte

       Pages 128-139

   12. Constraint Selection by Committee: An Ensemble Approach to Identifying Informative Constraints for Semi-supervised Clustering

       • Derek Greene, Pádraig Cunningham

       Pages 140-151

   13. The Cost of Learning Directed Cuts

       • Thomas Gärtner, Gemma C. Garriga

       Pages 152-163

   14. Spectral Clustering and Embedding with Hidden Markov Models

       • Tony Jebara, Yingbo Song, Kapil Thadani

       Pages 164-175

   15. Probabilistic Explanation Based Learning

       • Angelika Kimmig, Luc De Raedt, Hannu Toivonen

       Pages 176-187

The two premier annual European conferences in the areas of machine learning and data mining have been collocated ever since the ?rst joint conference in Freiburg, 2001. The European Conference on Machine Learning (ECML) traces its origins to 1986, when the ?rst European Working Session on Learning was held in Orsay, France. The European Conference on Principles and Practice of KnowledgeDiscoveryinDatabases(PKDD) was?rstheldin1997inTrondheim, Norway. Over the years, the ECML/PKDD series has evolved into one of the largest and most selective international conferences in machine learning and data mining. In 2007, the seventh collocated ECML/PKDD took place during September 17–21 on the centralcampus of WarsawUniversityand in the nearby Staszic Palace of the Polish Academy of Sciences. The conference for the third time used a hierarchical reviewing process. We nominated 30 Area Chairs, each of them responsible for one sub-?eld or several closely related research topics. Suitable areas were selected on the basis of the submission statistics for ECML/PKDD 2006 and for last year’s International Conference on Machine Learning (ICML 2006) to ensure a proper load balance amongtheAreaChairs.AjointProgramCommittee(PC)wasnominatedforthe two conferences, consisting of some 300 renowned researchers, mostly proposed by the Area Chairs. This joint PC, the largest of the series to date, allowed us to exploit synergies and deal competently with topic overlaps between ECML and PKDD. ECML/PKDD 2007 received 592 abstract submissions. As in previous years, toassistthereviewersandtheAreaChairsintheir?nalrecommendationauthors had the opportunity to communicate their feedback after the reviewing phase.

• active learning
• algorithmic learning
• algorithms
• classifier systems
• cognition
• genetic programming
• inductive inference
• kernel methods
• knowledge discovery
• machine learning
• multiple-instance learning
• optimization
• programming
• reinforcement learning
• uncertainty
• algorithm analysis and problem complexity

Policies and ethics