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Special Issue of JMLR on "New Perspectives on Kernel Based Learning Methods"

by mlang last modified 2007-07-26 21:53

The review process of the JMLR special issue is concluded. We had 28 submissions, 10 accepted papers, 36% of acceptance rate for this issue.

The list of accepted papers follows: (the papers will be made available after the authors have formatted them incorporating the required modifications):

  • Asa Ben-Hur David Horn Hava T. Siegelmann and Vladimir Vapnik: Support Vector Clustering
  • Roman Rosipal and Leonard J. Trejo: Kernel Partial Least Squares Regression in RKHS
  • Koby Crammer and Yoram Singer: On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines
  • T. Downs K.E. Gates and A. Masters: Simplifying Support Vector Solutions
  • Shai Fine and Katya Scheinberg: Efficient SVM Training Using Low-Rank Kernel Representation
  • Marc G. Genton: Classes of Kernels for Machine Learning: A Statistics Perspective
  • Claudio Gentile: A New Approximate Maximal Margin Classification Algorithm
  • Larry M. Manevitz and Malik Yousef: One-Class SVM for Document Classification
  • David M.J. Tax and Robert P.W. Duin: Uniform Object Generation for Optimizing One-Class Classifiers
  • Elzbieta Pekalska Pavel Paclik and Robert P.W. Duin: A Generalized Kernel Approach to Dissimilarity Based Classification
Guest Editors: Nello Cristianini, John Shawe-Taylor, Bob Williamson

Cf. also the kernel section of JMLR.

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