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Machine Learning and Knowledge Discovery in Databases

European Conference, Antwerp, Belgium, September 15-19, 2008, Proceedings, Part II

  • Conference proceedings
  • © 2008

Overview

Editors:
  1. Walter Daelemans
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  2. Bart Goethals
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  3. Katharina Morik
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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 5212)

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

Included in the following conference series:

Conference proceedings info: ECML PKDD 2008.

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Table of contents (48 papers)

  1. Regular Papers

    1. A Novel Scalable and Data Efficient Feature Subset Selection Algorithm

      • Sergio Rodrigues de Morais, Alex Aussem
      Pages 298-312

    2. Robust Feature Selection Using Ensemble Feature Selection Techniques

      • Yvan Saeys, Thomas Abeel, Yves Van de Peer
      Pages 313-325

    3. Effective Visualization of Information Diffusion Process over Complex Networks

      • Kazumi Saito, Masahiro Kimura, Hiroshi Motoda
      Pages 326-341

    4. Actively Transfer Domain Knowledge

      • Xiaoxiao Shi, Wei Fan, Jiangtao Ren
      Pages 342-357

    5. A Unified View of Matrix Factorization Models

      • Ajit P. Singh, Geoffrey J. Gordon
      Pages 358-373

    6. Parallel Spectral Clustering

      • Yangqiu Song, Wen-Yen Chen, Hongjie Bai, Chih-Jen Lin, Edward Y. Chang
      Pages 374-389

    7. Classification of Multi-labeled Data: A Generative Approach

      • Andreas P. Streich, Joachim M. Buhmann
      Pages 390-405

    8. Pool-Based Agnostic Experiment Design in Linear Regression

      • Masashi Sugiyama, Shinichi Nakajima
      Pages 406-422

    9. Distribution-Free Learning of Bayesian Network Structure

      • Xiaohai Sun
      Pages 423-439

    10. Assessing Nonlinear Granger Causality from Multivariate Time Series

      • Xiaohai Sun
      Pages 440-455

    11. Clustering Via Local Regression

      • Jun Sun, Zhiyong Shen, Hui Li, Yidong Shen
      Pages 456-471

    12. Decomposable Families of Itemsets

      • Nikolaj Tatti, Hannes Heikinheimo
      Pages 472-487

    13. Transferring Instances for Model-Based Reinforcement Learning

      • Matthew E. Taylor, Nicholas K. Jong, Peter Stone
      Pages 488-505

    14. A Simple Model for Sequences of Relational State Descriptions

      • Ingo Thon, Niels Landwehr, Luc De Raedt
      Pages 506-521

    15. Semi-Supervised Boosting for Multi-Class Classification

      • Hamed Valizadegan, Rong Jin, Anil K. Jain
      Pages 522-537

    16. A Joint Segmenting and Labeling Approach for Chinese Lexical Analysis

      • Xinhao Wang, Jiazhong Nie, Dingsheng Luo, Xihong Wu
      Pages 538-549

    17. Transferred Dimensionality Reduction

      • Zheng Wang, Yangqiu Song, Changshui Zhang
      Pages 550-565

    18. Multiple Manifolds Learning Framework Based on Hierarchical Mixture Density Model

      • Xiaoxia Wang, Peter Tiňo, Mark A. Fardal
      Pages 566-581

    19. Estimating Sales Opportunity Using Similarity-Based Methods

      • Sholom M. Weiss, Nitin Indurkhya
      Pages 582-596

    20. Learning MDP Action Models Via Discrete Mixture Trees

      • Michael Wynkoop, Thomas Dietterich
      Pages 597-612
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Other volumes

  1. Machine Learning and Knowledge Discovery in Databases

  2. Machine Learning and Knowledge Discovery in Databases

Keywords

About this book

This book constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2008, held in Antwerp, Belgium, in September 2008. The 100 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected from 521 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.

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