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ECML/PKDD 2004: Call for Papers

ECML/PKDD 2004 Announcement ruggieri at di.unipi.it
Sun Dec 21 23:38:58 EST 2003

Apologies for multiple posting

Call for Papers

Pisa, Italy, September 20-24, 2004
ecmlpkdd at isti.cnr.it

The 15th European Conference on Machine Learning (ECML) and the 8th European Conference 
on Principles and Practice of Knowledge Discovery in Databases (PKDD) will be co-located 
in Pisa, Italy, September 20-24, 2004. The combined event will comprise presentations of 
contributed papers and invited speakers, a wide program of workshops and tutorials, 
a demo session, and a discovery challenge.

Important dates 

- Submission deadline: Monday April 19, 2004 
- Notification of acceptance: Monday June 7, 2004
- Camera-ready copies due: Monday June 28, 2004 
- Conferences: Monday September 20 through Friday September 24, 2004 

Paper submission 

High quality research contributions pertinent to any aspects of machine learning and 
knowledge discovery are called for, ranging from principles to practice; particular 
attention will be paid to papers describing innovative, challenging  applications. 

There will be a single electronic submission procedure, where authors should indicate 
whether they submit their paper to ECML, PKDD, or both. In the latter case, the topic 
of the joint submission must be within the scope of both conferences; accepted joint 
submissions will be assigned to the most appropriate of the conferences. Student 
submissions should be clearly indicated on the submission form. All submissions will 
be reviewed by the respective program committees. 

The papers must  be in English and should be formatted according to the Springer-Verlag 
Lecture Notes in Artificial Intelligence guidelines. Authors instructions and style 
files can be downloaded at http://www.springer.de/comp/lncs/authors.html.  The maximum 
length of papers is 12 pages. The proceedings of ECML and PKDD will be published as two 
separate volumes by Springer-Verlag in the Lecture Notes in Artificial Intelligence 
series and will be available at the conference. 

Simultaneous submissions to other  conferences are allowed, provided this fact is clearly 
indicated on the submission form. Simultaneous submissions that are not clearly specified 
as such will be rejected.  Accepted papers will appear in the ECML/PKDD conference 
proceedings only if they are  withdrawn from proceedings of other conferences.

Best Paper Awards 

KDNet and Kluwer will honour the best papers and the best student papers with awards. 
The awards will be based on the significance and originality of the contributions.

ECML Call for Papers

The European Conference on Machine Learning series intends to provide an international forum 
for the discussion of the latest high quality research results in machine learning and is the 
major European scientific event in the field. Submissions of papers that describe the application 
of machine learning methods to real-world problems are encouraged, particularly exploratory 
research that describes novel learning tasks and applications requiring non-standard techniques. 
Submissions that demonstrate both theoretical and empirical rigor are especially encouraged. 

Topics of interest (non-exhaustive list): 

- artificial neural networks
- Bayesian networks 
- case-based reasoning 
- computational models of human learning 
- computational learning theory 
- cooperative learning 
- decision trees
- discovery of scientific laws
- evolutionary computation 
- grammatical inference
- incremental induction and on-line learning
- inductive logic programming 
- information retrieval and learning 
- instance based learning
- kernel methods  
- knowledge acquisition and learning 
- knowledge base refinement 
- knowledge intensive learning 
- machine learning of natural language 
- meta learning
- multi-agent learning 
- multi-strategy learning 
- planning and learning 
- reinforcement learning 
- revision and restructuring   
- statistical approaches 
- unsupervised learning 
- vision and learning

PKDD Call for Papers

Data Mining and Knowledge Discovery in Databases (KDD) is the ability to extract useful patterns 
from large amounts of data stored in databases, data warehouses or other information repositories. 
KDD is a combination of many research areas: databases, statistics, machine learning, automated 
scientific discovery, artificial intelligence, visualization, and high performance computing. KDD 
focuses on the value that is added by the creative combination of the contributing areas. 
The European Conference on Principles and Practice of Knowledge Discovery in Databases series 
intends to provide an international forum for the discussion of the latest high quality research 
results in KDD and is the major European scientific event in the field. Submissions are invited 
that describe empirical and theoretical research  in all areas of KDD, as well as submissions that 
describe challenging applications of KDD.

Topics of interest (non-exhaustive list): 

Algorithms and techniques
- classification
- clustering
- frequent patterns
- rule discovery
- statistical techniques and mixture models
- constraint-based mining
- incremental algorithms
- scalable algorithms
- distributed and parallel algorithms
- privacy preserving data mining
- multi-relational data mining
Data mining and databases
- database integration
- OLAP and data warehouse integration
- data mining query languages
- data mining query optimization
Data pre-processing
- dimensionality reduction
- data reduction
- discretization
- uncertain and missing information handling
Foundations of data mining
- complexity issues
- inductive databases
- knowledge (pattern) representation
- global vs. local patterns
- logic for data mining
- statistical inference and probabilistic modelling 
Innovative applications
- mining bio-medical data
- web content, structure and usage mining
- semantic web mining
- mining governmental data, mining for the public administration
- personalization 
- adaptive data mining architectures
- invisible data mining
KDD process and process-centric data mining
- models of the KDD process
- standards for the KDD process
- background knowledge integration
- collaborative data mining
- vertical data mining environments
Mining different forms of data
- graph, tree, sequence mining
- semi-structured and XML data mining
- text mining 
- temporal, spatial, and spatio-temporal data mining
- data stream mining
- multimedia mining
Pattern post-processing
- quality assessment
- visualization
- knowledge interpretation and use

Sponsors - initial list

ISTI-CNR, University of Pisa, INSA Lyon, University of Bari, Kluwer, KD-net

Committee and Chairs

Program Chairs: 

Jean-François Boulicaut, INSA Lyon, France
Floriana Esposito University of Bari, Italy
Fosca Giannotti KDDLab, ISTI-CNR, Pisa, Italy
Dino Pedreschi KDDLab, University of Pisa, Italy 

Workshop Chairs: 

Donato Malerba, University of Bari, Italy
Mohammed J. Zaki, Rensselaer Polytechnic Institute, USA 

Tutorial Chairs:  

Katharina Morik, University of Dortmund, Germany 
Franco Turini, KDDLab, University of Pisa, Italy

Discovery Challenge Chairs:

Petr Berka, Prague University of Economics, Czech Republic
Bruno Cremilleux, University of Caen, France

Publicity Chair:

Salvatore Ruggieri, KDDLab, University of Pisa, Italy

Demostration Committee: 

Elena Baralis, Politecnico of Torino, Italy 
Codrina Lauth, Fraunhofer AiS, Germany 
Rosa Meo, University of Torino, Italy 

Steering Committee: 

Hendrik Blockeel, Katholieke Universiteit Leuven, Belgium
Luc De Raedt, Albert-Ludwigs University Freiburg, Germany
Tapio Elomaa, Tampere University of Technology, Finland
Peter Flach, University of Bristol, UK
Dragan Gamberger, Rudjer Boskovic Institute, Croatia
Nada Lavrac, Jozef Stefan Institute, Slovenia
Heikki Mannila, Helsinki Institute for Information Technology, Finland
Arno Siebes, Utrecht University, The Netherlands
Ljupco Todorovski, Jozef Stefan Institute, Slovenia
Hannu T.T. Toivonen, University of Helsinki, Finland

Award Committee:

Floriana Esposito (PC representative)
Robert Holte, University of Alberta, Canada (Kluwer representative)
Michael May, Fraunhofer AiS, Germany (KDNet representative)

Organizing Committee:

Miriam Baglioni, KDDLab, University of Pisa, Italy
Jérémy Besson, INSA-Lyon, France
Francesco Bonchi, KDDLab, ISTI-CNR, Pisa, Italy
Stefano Ferilli, University of Bari, Italy
Tiziana Mazzone, KDDLab, Pisa,  Italy
Mirco Nanni, KDDLab, ISTI-CNR, Pisa, Italy
Ruggero Pensa, INSA-Lyon, France
Chiara Renso, KDDLab, ISTI-CNR, Pisa, Italy
Salvatore Rinzivillo, KDDLab, University of Pisa, Italy


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