Machine Learning: ECML 2006

• Boosting
• Support Vector Machine
• active learning
• algorithm
• algorithmic learning
• algorithms
• case-based learning
• classifier systems
• clustering algorithms
• knowledge discovery
• learning
• logic
• machine learning
• multiple-instance learning
• sensing
• algorithm analysis and problem complexity

From the reviews:

"In this book, we find many ways of representing machine learning from different fields, including active learning, algorithmic learning, case-based learning, classifier systems, clustering algorithms, decision-tree learning, inductive inference, kernel methods, knowledge discovery, multiple-instance learning, reinforcement learning, statistical learning, and support vector machines. Most of the current issues in machine learning research are discussed. … I strongly recommend this book for all researchers interested in the very best of machine learning studies." (Agliberto Cierco, ACM Computing Reviews, Vol. 49 (5), 2008)

• Knowledge Engineering Group, Technische Universität Darmstadt,  

  Johannes Fürnkranz

• Max Planck Institute for Computer Science, Saarbrücken, Germany

  Tobias Scheffer

• Faculty of Computer Science, Otto-von-Guericke-University Magdeburg, Germany

  Myra Spiliopoulou

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