Fw: Call for Papers, Special session on "Multi-Objective Machine Learning"

>
> Call for Papers
>
> Special Session on "Multi-objective Machine Learning"
> 2006 International Joint Conference on Neural Networks (part of WCCI'06)
> July 16-21, Vancouver, Canada
> http://www.wcci2006.org/
>
> Organized by Yaochu Jin (yaochu.jin@honda-ri.de)
> URL: http://www.soft-computing.de/CFP_SS_MOML.html
>
> Motivation and Scope:
>
> Machine learning usually has to achieve multiple targets, which are often
> conflicting with each other.
> For example in feature selection, minimizing the number of features and
the
> maximizing feature
> quality are two conflicting objectives. It is also well realized that
model
> selection has to deal with
> the trade-off between model complexity and approximation or classification
> accuracy.
> Traditional learning algorithms attempt to deal with multiple objectives
by
> combining them into a
> scalar cost function so that multi-objective machine learning problems are
> reduced to single-objective
> problems.
>
> Recently, increasing interest has been shown in applying Pareto-based
> multi-objective optimization
> to machine learning, particularly inspired by the successful developments
> in evolutionary multi-objective
> optimization. It has been shown that the multi-objective approach to
> machine learning is particularly
> successful in 1) improving the performance of the traditional
> single-objective machine learning
> methods 2) generating highly diverse multiple Pareto-optimal models for
> constructing ensembles and,
> 3) in achieving a desired trade-off between accuracy and interpretability
> of neural networks or fuzzy
> systems.
>
> This proposed special session intends to further promote research
interests
> in multi-objective machine
> learning by presenting the most recent research results and discussing the
> main challenges in this area. Topics
> include but are not limited to
>
> * multi-objective clustering, feature extraction and feature selection
> * multi-objective model selection to improve the performance of learning
> models, such as neural networks,
> support vector machines, decision trees, and fuzzy systems
> * multi-objective model selection to improve the interpretability of
> learning models, e.g., to extract
> symbolic rules from neural networks, or to improve the interpretability
> of fuzzy systems
> * multi-objective generation of learning ensembles
> * multi-objective learning to deal with tradeoffs between plasticity and
> stability, long-term and short-term
> memories, specialization and generalization
> * multi-objective machine learning applications
>
> Submission:
>
> All special session papers must be submitted no later than January 31,
2005
> through the conference
> webpage. Please notice me by sending me an email if you are interested in
> submitting a paper
> to the Special Session.
>
> ----------------------------------------------
> Dr. Yaochu Jin, Principal Scientist
> Honda Research Institute Europe
> Carl-Legien-Str.30
> 63073 Offenbach/Main
> Germany
> Phone: +49-69-89011735 Fax: +49-69-89011749
> Email: yaochu.jin@honda-ri.de
> ---------------------------------------------
Received on Thu Nov 10 10:26:29 2005