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