IEEE Trans. Neural Networks Special Issue: Online Learning in Kernel Methods

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Deadline: May 01, 2011 | Date: March 31, 2012

Venue/Country: Call for papers, Afghanistan

Updated: 2010-12-01 09:58:26 (GMT+9)

Call For Papers - CFP

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We invite original and unpublished research contributions in all areas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,

principecnel.ufl.edu

Dr. Seiichi Ozawa, Kobe University, Japan, ozawaseikobe-u.ac.jp

Dr. Sergios Theodoridis, University of Athens, Greece,

stheodordi.uoa.gr

Dr. Tülay Adali, University of Maryland, Baltimore County, USA,

adaliumbc.edu

Dr. Danilo P. Mandic, Imperial College London, UK,

d.mandicimperial.ac.uk

Dr. Weifeng Liu, Amazon.com, USA, weifengieee.org