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TNN-OLKM 2012 : IEEE Trans. Neural Networks Special Issue: Online Learning in Kernel Methods

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Link: http://ieee-cis.org/pubs/tnn/papers/
 
When Jan 1, 2011 - May 31, 2012
Where N/A
Submission Deadline May 1, 2011
Final Version Due May 31, 2012
Categories    kernel methods   machine learning   online learning   pattern recognition
 

Call For Papers

IEEE Trans. Neural Networks 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. All these techniques use algorithms that have to work with a large matrix (the Gram Matrix) which makes them computational and memory intensive.
It is theoretically possible to arrive at the neighborhood of the optimal solution using gradient descent techniques, with simpler and less memory intensive algorithms. There are already important algorithms in the literature that propose online learning with kernels such as resource allocating networks, growing and pruning radial basis function networks, kernel recursive least-squares algorithms, kernel least-mean-square algorithms, kernel affine projection algorithms, etc.
These advances are slowly evolving into a new adaptive system theory that can be encapsulated under the name of Online Learning in Kernel Methods (OLKM). OLKM uses Mercer kernels for nonlinear mapping of the input space into a hidden space of high dimensionality and uses linear adaptive structures (filters, regressors, classifiers) for accommodating the adaptive requirement. In so doing, it preserves the conceptual simplicity of linear adaptive filters (no local minima), and inherits the rich expressiveness from kernel methods (universal approximation property). Although OLKM has found applications in signal processing, pattern recognition, data mining, informational retrieval, and demand forecasting, the theory itself is far from complete. This special issue intends to attract papers that advance the mathematical foundations, the application and understanding of these methodologies.

We invite original and unpublished research contributions in all areas relevant to online learning with kernels.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

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