MLNET - a European network of excellence
in Machine Learning, Case-Based Reasoning and Knowledge Acquisition
MLNET is a "network of excellence" in Machine Learning that is funded by
the European Community to coordinate research, development and application
of Machine Learning, Case-Based Reasoning and Knowledge Acquisition in the
European Community. Activities of the network are:
* development and maintenance of an electronic information service that
gives access to information on: research groups, projects, datasets,
software, educational materials and links to other information services
(see http://www.mlnet.org/). This is the successor to the GMD information
service developed earlier by MLNET.
* development and maintenance of a scientific and technological outlook
("technological roadmap") for these areas that acts as a guideline for the
planning and coordination of research and development. (Members of MLNET
can take part in developing this.)
* development and maintenance of educational and "advertising" materials
for the areas of the network.
* organisation and support of events (e.g. European Conference on Machine
Learning, European Workshop on Case-Based Reasoning, European Knowledge
Acquisition Workshop, Multi-strategy learning workshop). Members of
MLNET can propose to organise such events and apply for support.
* funding of "ambassadors" who give presentations on Machine Learning,
Case-Based Reasoning or Knowledge Acquisition OUTSIDE the areas.
(proposals for "ambassador visits" by a member of MLNET can be submitted
to the coordinator of MLNET, see below).
Institutes and companies in the European community and associated states can
apply for membership. At the moment the information service is available for
everyone. Only members of MLNET can receive financial support for organising
or visiting MLNET events. MLNET collaborates with three other European
networks (ERUDIT on uncertainty modelling, EvoNet on evolutionary computing
and NEuroNet on neural network computing) on the theme Computational
Intelligence and Learning (see http://www.dcs.napier/coil/). More information
can be found at the MLNET Information Service.
- maarten van someren (coordinator of MLNET, maarten@swi.psy.uva.nl)
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