Call for papers : MODEL SELECTION
Isabelle Guyon and Amir Reza Saffari Azar Alamdari
The Journal of Machine Learning Research (JMLR) and the Journal
of Interesting Negative Results (JINR) are soliciting contributions
on the theme of Model Selection. Both journals will have a special topic
on Model Selection and cross-reference each other.
Model selection is a problem in statistics, machine learning, and data
mining. Given training data consisting of input-output pairs, a model is
built to predict the output from the input, usually by fitting adjustable
parameters. Many predictive models have been proposed to perform such tasks,
including linear models, neural networks, trees, and kernel methods. The
object of this call is to collect contributions on methods to optimally
select models, which will perform best on new test data (to be published
on JMLR). Experiments or theories demonstrating that commonly used methods
or promising published methods fail can be directed to JINR.
The call solicits papers on algorithms, experiments, and theories. It
covers all aspects of model selection, including supervised and unsupervised
learning, feature selection, pattern selection and hyper-parameter selection.
Contributions to multi-level inference (including multi-level optmization
and Bayesian methods) and ensemble methods are also within the scope of
the call. Another related problem is performance prediction. Predicting
accurately the generalization performances of the predictor is connected
to model selection because accurate performance predictions make good model
ranking criteria. Participants to the Performance Prediction Challenge,
and Model Selection Game, and are encouraged to submit papers. The
Model Selection Game uses the same datasets as the Performance Prediction
Challenge and is presently on-going, see the NIPS workshop page: http://clopinet.com/isabelle/Projects/NIPS2006/.
For publishing in JMLR: jmlr.org.
For publishing in JINR: jinr.org.
Please send a carbon copy of your paper to firstname.lastname@example.org.
Deadline: January 15th, 2007.