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Special Session at ICMLA 2011 : Special Session on 'Learning in evolving environments and its application on real-world problems'

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Link: http://www.icmla-conference.org/icmla11/LEE.htm
 
When Dec 18, 2011 - Dec 21, 2011
Where Honolulu, Hawaii
Submission Deadline Jul 15, 2011
Notification Due Sep 2, 2011
Final Version Due Oct 1, 2011
Categories    machine learning   evolving systems   incremental learning   active learning
 

Call For Papers

CALL FOR PAPERS

Special Session on: 'Learning in evolving environments and its application on real-world problems'

to be organized at the
IEEE 10th International Conference on Machine Learning and Applications (ICMLA 2011),
Honolulu, Hawaii, USA, 18-21 Dec. 2011
Website: http://www.icmla-conference.org/icmla11/


IMPORTANT DATES
Paper Submission Deadline: July 15, 2011
Notification of acceptance: September 2, 2011
Camera-ready papers & Pre-registration: October 1, 2011
The ICMLA Conference: December 18-21, 2011


Organizers:
Moamar Sayed-Mouchaweh, University of Reims-France, moamar.sayed-mouchaweh@univ-reims.fr
Edwin Lughofer, University of Linz, Austria, edwin.lughofer@jku.at

Authors of the accepted papers will be invited to submit an expanded and revised version of their paper to be published in a special issue of the Springer journal “Evolving Systems”:
http://www.springer.com/engineering/journal/12530

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AIMS AND SCOPE

The computerization of many life activities and the advances in data collection and storage technology lead to obtain mountains of data. They are collected to capture information about a phenomena or a process behavior. These data are rarely of direct benefit. Thus, a set of techniques and tools are used to extract useful information for decision support, prediction, exploration and understanding of phenomena governing the data sources.

Learning methods use historic data points about a process past behavior to build a predictor (classifier, regression model). The latter is used as an old experience to predict the process future behavior. However, the predictor needs to adjust itself (self-correction or adaptation) as new events happen or new conditions/system states occur (e.g. during on-line operations). The goal is to ensure an accurate prediction of process behavior according to the changes in new incoming data characteristics. This requires a continuous learning over long period of time with the ability to evolve new structural components on demand and to forget data becoming obsolete and useless. Incremental and sequential learning are essential concepts in order to avoid time-intensive re-training phases and account for the systems dynamics/changing data characteristics with low computational effort and virtual memory usage (enhancing on-line performance). This is because data is processed in sample-wise and single-pass manner.

It is important that updates of model parameters and structure is achieved without a “catastrophic forgetting”. Therefore, a balance between continuous learning and “forgetting” is necessary to deal with non-stationary environments.

This special session looks to gather and discuss efficient techniques, methods and tools able to manage, to exploit and to interpret correctly the increasing amount of data in environments that are continuously changing. The goal is to build models for predicting the future system behavior, able to tackle and to govern the high variability of complex non-stationary systems.


TOPICS

This session solicits original research papers including but not limited to the following topics:

· Incremental learning methods

· Adaptive, life-long and sequential learning

· On-line classification and regression methods

· Evolving structural components and system modelling

· Incremental/evolving un-supervised methods

· Incremental/on-line dimensionality reduction methods

· Concepts to address drifts and shifts in data streams (weighting, gradual forgetting, etc.)

· On-line/incremental active and semi-supervised learning

· On-line human-machine interaction and the incorporation of background knowledge

· Adaptive data pre-processing and knowledge discovery

· Applications of dynamic/on-line/incremental learning for:

o Monitoring,

o On-line quality control systems

o Fault detection and isolation

o Huge data bases

o Web applications

o Decision Support Systems

o And many more ….



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