Machine Learning and Knowledge Discovery in Databases

1. Front Matter

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2. Invited Talks (Abstracts)

   1. Theory-Practice Interplay in Machine Learning – Emerging Theoretical Challenges

      • Shai Ben-David

      Pages 1-1

   2. Are We There Yet?

      • Nello Cristianini

      Pages 2-2

   3. The Growing Semantic Web

      • Mark Greaves

      Pages 3-3

   4. Privacy in Web Search Query Log Mining

      • Rosie Jones

      Pages 4-4

   5. Highly Multilingual News Analysis Applications

      • Ralf Steinberger

      Pages 5-5

3. Machine Learning Journal Abstracts

   1. Combining Instance-Based Learning and Logistic Regression for Multilabel Classification

      • Weiwei Cheng, Eyke Hüllermeier

      Pages 6-6

   2. On Structured Output Training: Hard Cases and an Efficient Alternative

      • Thomas Gärtner, Shankar Vembu

      Pages 7-7

   3. Sparse Kernel SVMs via Cutting-Plane Training

      • Thorsten Joachims, Chun-Nam John Yu

      Pages 8-8

   4. Hybrid Least-Squares Algorithms for Approximate Policy Evaluation

      • Jeff Johns, Marek Petrik, Sridhar Mahadevan

      Pages 9-9

   5. A Self-training Approach to Cost Sensitive Uncertainty Sampling

      • Alexander Liu, Goo Jun, Joydeep Ghosh

      Pages 10-10

   6. Learning Multi-linear Representations of Distributions for Efficient Inference

      • Dan Roth, Rajhans Samdani

      Pages 11-11

   7. Cost-Sensitive Learning Based on Bregman Divergences

      • Raúl Santos-Rodríguez, Alicia Guerrero-Curieses, Rocío Alaiz-Rodríguez, Jesús Cid-Sueiro

      Pages 12-12

4. Data Mining and Knowledge Discovery Journal Abstracts

   1. RTG: A Recursive Realistic Graph Generator Using Random Typing

      • Leman Akoglu, Christos Faloutsos

      Pages 13-28

   2. Taxonomy-Driven Lumping for Sequence Mining

      • Francesco Bonchi, Carlos Castillo, Debora Donato, Aristides Gionis

      Pages 29-29

   3. On Subgroup Discovery in Numerical Domains

      • Henrik Grosskreutz, Stefan Rüping

      Pages 30-30

   4. Harnessing the Strengths of Anytime Algorithms for Constant Data Streams

      • Philipp Kranen, Thomas Seidl

      Pages 31-31

   5. Identifying the Components

      • Matthijs van Leeuwen, Jilles Vreeken, Arno Siebes

      Pages 32-32

   6. Two-Way Analysis of High-Dimensional Collinear Data

      • Ilkka Huopaniemi, Tommi Suvitaival, Janne Nikkilä, Matej Orešič, Samuel Kaski

      Pages 33-33

   7. A Fast Ensemble Pruning Algorithm Based on Pattern Mining Process

      • Qiang-Li Zhao, Yan-Huang Jiang, Ming Xu

      Pages 34-34

This book constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2009, held in Bled, Slovenia, in September 2009. The 106 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected from 422 paper submissions. In addition to the regular papers the volume contains 14 abstracts of papers appearing in full version in the Machine Learning Journal and the Knowledge Discovery and Databases Journal of Springer. The conference intends to provide an international forum for the discussion of the latest high quality research results in all areas related to machine learning and knowledge discovery in databases. The topics addressed are application of machine learning and data mining methods to real-world problems, particularly exploratory research that describes novel learning and mining tasks and applications requiring non-standard techniques.

• classification
• clustering
• data mining
• database
• graph mining
• information retrieval
• knowledge discovery
• machine learning
• neural networks
• privacy
• reinforcement learning
• semantic web
• visualization
• web mining

• NICTA, Locked Bag 8001, Canberra, 2601, Australia and Helsinki Institute of IT,, Finland

  Wray Buntine

• Dept. of Knowledge Technologies, Jožef Stefan Institute, Ljubljana, Slovenia

  Marko Grobelnik, Dunja Mladenić

• University College London, The Centre for Computational Statistics and Machine Learning Department of Computer Science, London, UK

  John Shawe-Taylor

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