## CALL FOR PAPERS

## Special Topic on Machine Learning and Large Scale Optimization

### Deadline Extended to October 5, 2005

Guest Editors:
Kristin P. Bennett,
Emilio Parrado-Hernández
http://www.jmlr.org/cfp/mllso.html

We invite papers on the combination of machine learning and large
scale optimization for a special topic of the Journal of Machine
Learning Research (JMLR). This topic motivated the PASCAL (Pattern
Analysis, Statistical Modelling and Computational Learning Network
of Excellence) Workshop on "Machine Learning, SVM and Large Scale
Optimization", celebrated in Thurnau, Germany from March 16 to 18, 2005.
All participants in the workshop are invited to submit a full paper,
but submission is open to everyone.

Many modern machine learning algorithms reduce to solving large-scale
linear, quadratic or semi-definite mathematical programming problems.
Optimization has thus become a crucial tool for learning, and learning
a major application of optimization. Furthermore, a systematic recasting of learning and estimation problems in the
framework of mathematical programming has encouraged the use of
advanced techniques from optimization such as convex analysis,
Lagrangian duality and large scale linear algebra. This has allowed
much sharper theoretical analyses, and greatly increased the size and
range of problems that can be handled. Several key application domains
have developed explosively, notably text and web analysis, machine
vision, and speech all fuelled by ever expanding data resources easily
accessible via the web.

This special topic is intended to bring closer optimization and
machine learning communities for further
algorithmic progress, particularly for developing large-scale learning
methods capable of handling massive document and image datasets.

Topics of interest include:

- Mathematical programming approaches to machine learning
problems, like semi-definite programming, interior point methods,
sequential convex programming, gradient-based methods, etc.
- Optimisation on graphical models for machine learning, belief
propagation.
- Efficient training of Support Vector Machines, incremental SVMs,
optimization over kernels.
- Convex relaxations of machine learning problems.
- Applications involving large scale databases, such as data
mining, bioinformatics, multimedia.

Submission procedure:
Submit papers to standard JMLR submission system

http://jmlr.csail.mit.edu/manudb

Please include a note stating that your submission is for the special
topic on Machine Learning and Large Scale Optimization.

Important Dates:

- First version due: Octoboer 5, 2005 (extended from September 15, 2005.)
- First notification of acceptance or rejection: November 23, 2005
- Second version due: January 6, 2006
- Final notification of acceptance: February 15, 2006
- Final version due: March 17, 2006.

For further details or enquires, please contact the guest editors:
Kristin P. Bennett (bennek@rpi.edu)

Emilio Parrado-Hernández( emipar@tsc.uc3m.es)