IEEE TNN Special Issue: Online Learning in Kernel Methods

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 We invite original and unpublished research contributions in all areas relevant to online learning with kernels.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:

• Online learning for kernel adaptive systems
• Kernelization of online learning techniques
• Optimization, growing and pruning techniques and kernel design for online kernel learning
• Information theoretic learning principles in kernel adaptive systems
• Multidimensional kernel adaptive systems (complex, quaternion, and multichannel)
• Convergence, steady-state and error bound analysis of online kernel algorithms
• New applications of online learning with kernels

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

Guest editors:

Dr. Jose C. Principe, University of Florida, USA, principe@cnel.ufl.edu

Dr. Seiichi Ozawa, Kobe University, Japan, ozawasei@kobe-u.ac.jp

Dr. Sergios Theodoridis, University of Athens, Greece, stheodor@di.uoa.gr

Dr. Tülay Adali, University of Maryland, Baltimore County, USA, adali@umbc.edu

Dr. Danilo P. Mandic, Imperial College London, UK, d.mandic@imperial.ac.uk

Dr. Weifeng Liu, Amazon.com, USA, weifeng@ieee.org