Special Issue of JMLR on "New Perspectives on Kernel Based Learning Methods"
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
Cf. also the kernel section of JMLR.