The Fourth International Workshop on Pattern Recognition in Information Systems
– PRIS 2004

http://www.iceis.org/workshops/pris/pris2004-cfp.html

April 13-14, 2004 – Porto, Portugal

Deadline for paper submission: December 15, 2003

In conjunction with the Sixth International Conference on Enterprise Information
Systems - ICEIS 2004

In collaboration with The International Association for Pattern Recognition (IAPR)

Chair: Ana Fred, Instituto Superior Ticnico, Lisbon, Portugal (afred@lx.it.pt)

Workshop Background and Goals

Huge amounts of information in digital format are currently available; for
example, the world-wide-web is an immense source of text, images, video, and
sound. Structuring/understanding this multimedia information is a difficult task.
Accessibility issues arise in this context, with personal identification
techniques playing a key role in human-machine interfaces for security and
restricted-access systems. Pattern recognition (PR) and machine learning (ML)
provide formal frameworks in which this class of problems can be adequately
addressed. On the other hand, exploratory analysis (mining) and content-based
retrieval of multimedia data serve both as important practical application domains
for PR and ML techniques, and as a source of challenging research problems.

The aim of this workshop is to bring together researchers, practitioners and
potential users with interests in the multidisciplinary topics listed above.

Areas of Interest

Paper submissions should be related to the following areas: pattern recognition,
biometrics and personal identification; multimedia storage and retrieval; and data
mining and machine learning. Focus on applications (industrial, medical,
entertainment, home media) is desirable, although new techniques and algorithms
are also highly encouraged.

Topics of interest include, but are not limited to:
- Data mining
- Multimedia information systems
- Content-Based analysis and Retrieval
- Data categorization (text documents and images)
- Document analysis and classification
- Feature extraction and selection (for IP applications)
- Biometrics and personal authentication systems
- Learning systems and learning repositories
- Databases, and web-based information systems
- Data and knowledge representation
- Efficient and scalable learning
- Human computer interaction
- User profile modelling
- Quality assessment and performance measurement

Techniques:
- Classification algorithms
- Unsupervised learning
- Combination techniques
- Nonparametric methods
- Prototype-based techniques
- Structural Matching
- Neural-based pattern recognition
- Graph-based methods
- Genetic techniques
- Grammar and language methods
- Grammatical inference
- Comparative studies
- Hybrid methods