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10 years of news and resources for members of the IEEE Signal Processing Society
Online learning is one of the most powerful and commonly used techniques for training adaptive filters and has been used successfully in neural networks. The last decade has also witnessed a flurry of research efforts in Mercer kernel methods, such as the SVM and kernel regression, kernel principal component analysis etc. We invite original and unpublished research contributions in all areas relevant to online learning with kernels. The papers will present original work or review state-of-the-art approaches that summarize the recent advances in the following non-exhaustive list of topics:
Prospective authors should visit http://ieee-cis.org/pubs/tnn/papers/ for information on paper submission. Manuscripts should be submitted using the Manuscript Central system at http://mc.manuscriptcentral.com/tnn. On the first page of the manuscript as well as on the cover letter, indicate clearly that the manuscript is submitted to the TNN Special Issue: Online Kernel Learning. Manuscripts will be peer reviewed according to the standard IEEE process.
Manuscript submission due: May 1, 2011
First review completed: October 1, 2011
Revised manuscript due: December 1, 2011
Second review completed: March 1, 2012
Final manuscript due: March 31, 2012
Dr. Jose C. Principe, University of Florida, USA, email@example.com
Dr. Seiichi Ozawa, Kobe University, Japan, firstname.lastname@example.org
Dr. Sergios Theodoridis, University of Athens, Greece, email@example.com
Dr. Tülay Adali, University of Maryland, Baltimore County, USA, firstname.lastname@example.org
Dr. Danilo P. Mandic, Imperial College London, UK, email@example.com
Dr. Weifeng Liu, Amazon.com, USA, firstname.lastname@example.org
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