Call For Papers: Special Topic on Machine Learning and Large Scale Optimization

CALL FOR PAPERS

Journal of Machine Learning Research

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)