Machine Learning and Knowledge Discovery in Databases

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

  • Peter A. Flach
  • Tijl De Bie
  • Nello Cristianini

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

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 7523)

Table of contents

  1. Front Matter
  2. Invited Talks

  3. Association Rules and Frequent Patterns

  4. Bayesian Learning and Graphical Models

    1. Maxime Gasse, Alex Aussem, Haytham Elghazel
      Pages 58-73
    2. Sebastian Tschiatschek, Peter Reinprecht, Manfred Mücke, Franz Pernkopf
      Pages 74-89
    3. Tivadar Pápai, Shalini Ghosh, Henry Kautz
      Pages 90-105
    4. Shengbo Guo, Scott Sanner, Thore Graepel, Wray Buntine
      Pages 106-121
  5. Classification

  6. Dimensionality Reduction, Feature Selection and Extraction

    1. Francis Maes, Pierre Geurts, Louis Wehenkel
      Pages 191-206
    2. Zhihong Zhang, Edwin R. Hancock, Xiao Bai
      Pages 207-222

Other volumes

  1. Machine Learning and Knowledge Discovery in Databases
    European Conference, ECML PKDD 2012, Bristol, UK, September 24-28, 2012. Proceedings, Part I
  2. European Conference, ECML PKDD 2012, Bristol, UK, September 24-28, 2012. Proceedings, Part II

About these proceedings

Introduction

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.

Keywords

Bayesian network classifiers data mining hypergraphs social media tree search

Editors and affiliations

  • Peter A. Flach
    • 1
  • Tijl De Bie
    • 1
  • Nello Cristianini
    • 1
  1. 1.Intelligent Systems LaboratoryUniversity of BristolBristolUK

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-33460-3
  • Copyright Information Springer-Verlag Berlin Heidelberg 2012
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-642-33459-7
  • Online ISBN 978-3-642-33460-3
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • About this book
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