JMLR 2011 - Journal of Machine Learning Research Special Topic on Kernel and Metric Learning

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Category JMLR 2011

Deadline: March 01, 2011 | Date: July 01, 2011

Venue/Country: Call for papers, Afghanistan

Updated: 2010-10-21 09:36:19 (GMT+9)

Call For Papers - CFP

Special Topic on Kernel and Metric Learning

Multiple Kernel Learning (MKL) has received significant interest in

the machine learning community. It is reaching a point where efficient

systems can be applied out of the box to various application domains,

and several methods have been proposed to go beyond canonical convex

combinations. Concurrently, research in the area of metric learning

has also progressed significantly, and researchers are applying them

to various problems in supervised and unsupervised learning. A common

theme is that one can use data to infer similarities between objects

while simultaneously solving the machine learning task.

jmlr

A special topic of the Journal of Machine Learning Research will be

devoted to kernel and metric learning with a special emphasis on new

directions and connections between the various related areas; like

learning the kernel, learning metrics, and learning the covariance

function of a Gaussian process. We invite researchers to submit novel

and interesting contributions to this special issue. Further

information can be found at http://doc.ml.tu-berlin.de/jmlr_mkl .

Important dates

Submission: 1 March 2011

Decision: 1 May 2011

Final versions: 1 July 2011

Topics of Interest

Topics of interest include:

* New approaches to MKL, in particular, kernel parameterizations

different than convex combinations and new objective functions

* New connections between kernel, metric and covariance learning,

e.g., from the perspectives of Gaussian processes, learning with

similarity functions, etc.

* Sparse vs. non-sparse regularization in similarity learning

* Efficient algorithms for metric learning

* Use of MKL in unsupervised, semi-supervised, multi-task, and

transfer learning

* MKL with structured input/output

* Innovative applications

Submission procedure

Authors are kindly invited to follow the standard JMLR format and

submission procedure JMLR submission format, the number of pages is

limited to 30. Please include a note stating that your submission is for

the special topic on Multiple Kernel Learning.

Editors

Soeren Sonnenburg, Berlin Institute of Technology, Berlin, Germany

Francis Bach, INRIA and Ecole Normale Superieure, Paris, France

Cheng Soon Ong, ETH, Zurich, Switzerland

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Soeren Sonnenburg - ML Group, TU-Berlin Tel: +49 (0)30 314 78630

Franklinstr. 28/29, 10587 Berlin, Germany Fax: +49 (0)30 314 78622