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Machine Learning and Knowledge Discovery in Databases

European Conference, ECML PKDD 2012, Bristol, UK, September 24-28, 2012. Proceedings, Part I

  • Conference proceedings
  • © 2012

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Editors:
  1. Peter A. Flach

    1. Intelligent Systems Laboratory, University of Bristol, Bristol, UK

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  2. Tijl Bie

    1. Intelligent Systems Laboratory, University of Bristol, Bristol, UK

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  3. Nello Cristianini

    1. Intelligent Systems Laboratory, University of Bristol, Bristol, UK

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 7523)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

Included in the following conference series:

Conference proceedings info: ECML PKDD 2012.

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Table of contents (60 papers)

  1. Front Matter

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  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

      • Ãœrün 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
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Other volumes

  1. Machine Learning and Knowledge Discovery in Databases

  2. Machine Learning and Knowledge Discovery in Databases

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About this book

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.

Editors and Affiliations

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

    Peter A. Flach, Tijl Bie, Nello Cristianini

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