Machine Learning and Knowledge Discovery in Databases

1. Front Matter

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2. Invited Talks

   1. Machine Learning for Robotics

      • Pieter Abbeel

      Pages 1-1

   2. Declarative Modeling for Machine Learning and Data Mining

      • Luc De Raedt

      Pages 2-3

   3. Machine Learning Methods for Music Discovery and Recommendation

      • Douglas Eck

      Pages 4-4

   4. Solving Problems with Visual Analytics: Challenges and Applications

      • Daniel Keim

      Pages 5-6

   5. Analyzing Text and Social Network Data with Probabilistic Models

      • Padhraic Smyth

      Pages 7-8

3. Association Rules and Frequent Patterns

   1. Discovering Descriptive Tile Trees

      • Nikolaj Tatti, Jilles Vreeken

      Pages 9-24

   2. Efficient Discovery of Association Rules and Frequent Itemsets through Sampling with Tight Performance Guarantees

      • Matteo Riondato, Eli Upfal

      Pages 25-41

   3. Smoothing Categorical Data

      • Arno Siebes, René Kersten

      Pages 42-57

4. Bayesian Learning and Graphical Models

   1. An Experimental Comparison of Hybrid Algorithms for Bayesian Network Structure Learning

      • Maxime Gasse, Alex Aussem, Haytham Elghazel

      Pages 58-73

   2. Bayesian Network Classifiers with Reduced Precision Parameters

      • Sebastian Tschiatschek, Peter Reinprecht, Manfred Mücke, Franz Pernkopf

      Pages 74-89

   3. Combining Subjective Probabilities and Data in Training Markov Logic Networks

      • Tivadar Pápai, Shalini Ghosh, Henry Kautz

      Pages 90-105

   4. Score-Based Bayesian Skill Learning

      • Shengbo Guo, Scott Sanner, Thore Graepel, Wray Buntine

      Pages 106-121

5. Classification

   1. A Note on Extending Generalization Bounds for Binary Large-Margin Classifiers to Multiple Classes

      • Ürün Dogan, Tobias Glasmachers, Christian Igel

      Pages 122-129

   2. Extension of the Rocchio Classification Method to Multi-modal Categorization of Documents in Social Media

      • Amin Mantrach, Jean-Michel Renders

      Pages 130-142

   3. Label-Noise Robust Logistic Regression and Its Applications

      • Jakramate Bootkrajang, Ata Kabán

      Pages 143-158

   4. Sentiment Classification with Supervised Sequence Embedding

      • Dmitriy Bespalov, Yanjun Qi, Bing Bai, Ali Shokoufandeh

      Pages 159-174

   5. The Bitvector Machine: A Fast and Robust Machine Learning Algorithm for Non-linear Problems

      • Stefan Edelkamp, Martin Stommel

      Pages 175-190

6. Dimensionality Reduction, Feature Selection and Extraction

   1. Embedding Monte Carlo Search of Features in Tree-Based Ensemble Methods

      • Francis Maes, Pierre Geurts, Louis Wehenkel

      Pages 191-206

   2. Hypergraph Spectra for Semi-supervised Feature Selection

      • Zhihong Zhang, Edwin R. Hancock, Xiao Bai

      Pages 207-222

This two-volume set LNAI 7523 and LNAI 7524 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2012, held in Bristol, UK, in September 2012. The 105 revised research papers presented together with 5 invited talks were carefully reviewed and selected from 443 submissions. The final sections of the proceedings are devoted to Demo and Nectar papers. The Demo track includes 10 papers (from 19 submissions) and the Nectar track includes 4 papers (from 14 submissions). The papers grouped in topical sections on association rules and frequent patterns; Bayesian learning and graphical models; classification; dimensionality reduction, feature selection and extraction; distance-based methods and kernels; ensemble methods; graph and tree mining; large-scale, distributed and parallel mining and learning; multi-relational mining and learning; multi-task learning; natural language processing; online learning and data streams; privacy and security; rankings and recommendations; reinforcement learning and planning; rule mining and subgroup discovery; semi-supervised and transductive learning; sensor data; sequence and string mining; social network mining; spatial and geographical data mining; statistical methods and evaluation; time series and temporal data mining; and transfer learning.

• Bayesian network classifiers
• data mining
• hypergraphs
• social media
• tree search

• Intelligent Systems Laboratory, University of Bristol, Bristol, UK

  Peter A. Flach, Tijl Bie, Nello Cristianini

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