Statistical Analysis, Machine Learning and Image Analysis - SAMBA
The SAMBA department has comprehensive theoretical and practical knowledge in the fields of statistics, machine learning and image analysis. We are one of Europe's largest and most competent groups within applied statistics and statistical-matematical modelling. We cover a broad spectrum of methods and are a world leader in some of these areas. The appropriate choice of method for the various problems is thus one of our strengths. Many calculations involve uncertainty and the accurate calculation of this quantity is an important speciality.
Last 5 scientific articles
Deilkås, Ellen C Tveter; Risberg, Madeleine Borgstedt; Haugen, Marion; Lindstrøm, Jonas Christoffer; Nylén, Urban; Rutberg, Hans; Soop, Michael. Exploring similarities and differences in hospital adverse event rates between Norway and Sweden using Global Trigger Tool. BMJ Open (ISSN 2044-6055). 7(3) doi: 10.1136/bmjopen-2016-012492. 2017. Full-text
Løkse, Sigurd; Bianchi, Filippo Maria; Salberg, Arnt Børre; Jenssen, Robert. Spectral clustering using PCKID ? A probabilistic cluster kernel for incomplete data. Lecture Notes in Computer Science (ISSN 0302-9743). 10269 LNCS pp 431-442. doi: 10.1007/978-3-319-59126-1_36. 2017.
Tvete, Ingunn Fride; Bjørner, Trine; Skomedal, Tor. New benzodiazepine and Z-hypnotic users and disability pension: an eight-year nationwide observational follow-up study. Scandinavian Journal of Primary Health Care (ISSN 0281-3432). doi: 10.1080/02813432.2017.1358436. 2017. Full-text
Lison, Pierre; Bibauw, Serge. Not All Dialogues are Created Equal: Instance Weighting for Neural Conversational Models. In: 18th Annual SIGdial Meeting on Discourse and Dialogue (SIGDIAL 2017). (ISBN 978-1-945626-82-1). pp 384-394. 2017. Full-text