Subspace, Latent Structure and Feature Selection

Statistical and Optimization Perspectives Workshop, SLSFS 2005, Bohinj, Slovenia, February 23-25, 2005, Revised Selected Papers

  • Craig Saunders
  • Marko Grobelnik
  • Steve Gunn
  • John Shawe-Taylor

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3940)

Table of contents

  1. Front Matter
  2. Invited Contributions

    1. Wray Buntine, Aleks Jakulin
      Pages 1-33
    2. Roman Rosipal, Nicole Krämer
      Pages 34-51
    3. D. M. Titterington
      Pages 69-83
    4. Dunja Mladenić
      Pages 84-102
  3. Contributed Papers

    1. Florent Monay, Pedro Quelhas, Daniel Gatica-Perez, Jean-Marc Odobez
      Pages 115-126
    2. Amir Navot, Ran Gilad-Bachrach, Yiftah Navot, Naftali Tishby
      Pages 127-138
    3. Charles Bouveyron, Stéphane Girard, Cordelia Schmid
      Pages 139-150
    4. Christian Savu-Krohn, Peter Auer
      Pages 163-172
    5. Jeremy Rogers, Steve Gunn
      Pages 173-184
  4. Back Matter

About these proceedings

Keywords

3D Bayesian inference STATISTICA algorithm algorithmic learning algorithms calculus image reconstruction learning machine learning optimisation methods optimization statistical analysis statistical learning statistical modeling

Editors and affiliations

  • Craig Saunders
    • 1
  • Marko Grobelnik
    • 2
  • Steve Gunn
    • 3
  • John Shawe-Taylor
    • 4
  1. 1.ISIS Research GroupUniversity of SouthamptonU.K.
  2. 2.Dept. of Knowledge TechnologiesJozef Stefan InstituteLjubljanaSlovenia
  3. 3.School of Electronics and Computer ScienceUniversity of SouthamptonSouthamptonUK
  4. 4.The Centre for Computational Statistics and Machine Learning Department of Computer ScienceUniversity College LondonLondonUK

Bibliographic information

  • DOI https://doi.org/10.1007/11752790
  • Copyright Information Springer-Verlag Berlin Heidelberg 2006
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-540-34137-6
  • Online ISBN 978-3-540-34138-3
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • About this book