The main goal of this workshop is to assess the state of the art of the theory and the applications of multiple classifier systems and related approaches.

Contributions from all the research communities working in the field are welcome in order to compare the different approaches and to define the common research priorities.

Special attention is also devoted to assess the applications of multiple classifier systems and the potential market perspectives.

The workshop program will include both plenary lectures given by invited speakers and papers accepted for oral presentation.

Accepted papers will appear in the workshop proceedings that will be published in the series Lecture Notes in Computer Science by Springer-Verlag.

Extended versions of selected papers will be considered for publication in a special issue of the Pattern Analysis and Applications Journal on Classifier Fusion.

Papers describing original work in the following and related research topics are welcome:

• Theoretical foundations of multiple classifier systems
• Methods for classifier combination
• Methods for classifier selection
• Neural network ensembles
• Modular neural networks
• Mixture models
• Multiple expert systems
• Hybrid systems
• Learning in multiple classifier systems
• Design of multiple classifier systems
• Multiple models in data mining
• Related approaches (intelligent agents, multi-criteria decision making, etc.)
• Applications (biometrics, document analysis, data mining, remote sensing, etc.)