A thorough analysis of similarity measures for learning from heterogeneous data sources

01 October 2007 → 01 October 2008
Regional and community funding: Special Research Fund
Research disciplines
  • Natural sciences
    • Applied mathematics in specific fields
    • Artificial intelligence
    • Scientific computing
    • Bioinformatics and computational biology
  • Social sciences
    • Cognitive science and intelligent systems
  • Medical and health sciences
    • Bioinformatics and computational biology
    • Bioinformatics and computational biology
    • Public health care
    • Public health services
    • Bioinformatics and computational biology
  • Engineering and technology
    • Scientific computing
kernel methods machine learning similarity measures classification clustering
Project description

In this project we will investigate the use of similarity measures for machine learning from heterogeneous data sources. A special class of similarity measures are the kernel methods, which will be thoroughly analysed. This project aims to stimulate the development of new information systems, allowing the richness of current data sources to be used more efficiently.