SAMBA
SAMBA
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.
Research areas
Our research within statistics for climate and environment is a mixture of applied, problem-driven projects and fundamental research eligible for publication in scientific journals.
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We use statistical methods to quantify all kinds of risk in the banking, insurance and commodity markets. We work a lot with models related to Basel II and Solvency II and have developed a large number of systems related to trading and risk management for various power companies.
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We cover a wide range of applications within the areas marine resources and health.
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We cover a wide range of applications including forecasting, time series analysis, network modelling, optimal delivery, logistics, optimal steering of industry processes, reliability analysis, cost surveys and analysis of very large data sets.
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We do research and development of methodologies for analysis of digital remote sensing data from satellites and aircrafts. Application areas include environmental monitoring, climate change monitoring, environmental emergency detection, natural resource mapping and map revision.
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We have extensive knowledge in the fields of image analysis and machine learning. Object extraction, characterization and recognition from images are central for many of these applications. Another central theme is the analysis of sequences of data, including motion detection and characterization.
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This center for research-based-innovation is funded by the Research Council of Norway. Big Insight produces innovative solutions for key challenges facing our consortium of private, public and research partners, by developing original statistical and machine learning methodologies.
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Last 5 scientific articles
Kampffmeyer, Michael C.; Løkse, Sigurd; Bianchi, Filippo Maria; Livi, Lorenzo; Salberg, Arnt Børre; Jenssen, Robert. Deep divergence-based approach to clustering. Neural Networks (ISSN 0893-6080). 113 pp 91-101. doi: 10.1016/j.neunet.2019.01.015. 2019.
Revhaug, Cecilie; Zasada, Magdalena; Rognlien, Anne Gro Wesenberg; Günther, Clara-Cecilie; Grabowska, Agnieszka; Książek, Teofila; Madetko-Talowska, Anna; Szewczyk, Katarzyna; Bik-Multanowski, Miroslaw; Kwinta, Przemko; Pietrzyk, Jacek Józef; Baumbusch, Lars Oliver; Saugstad, Ola Didrik. Pulmonary vascular disease is evident in gene regulation of experimental bronchopulmonary dysplasia. Journal of Maternal-Fetal & Neonatal Medicine (ISSN 1476-7058). pp 1-10. doi: 10.1080/14767058.2018.1541081. 2019.
Hubin, Aliaksandr; Storvik, Geir Olve; Frommlet, Florian. A Novel Algorithmic Approach to Bayesian Logic Regression. Bayesian Analysis (ISSN 1936-0975). doi: 10.1214/18-BA1141. 2018.
Liu, Qinghui; Salberg, Arnt Børre; Jenssen, Robert. A Comparison of Deep Learning Architectures for Semantic Mapping of Very High Resolution Images. In: IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. (ISBN 978-1-5386-7150-4). pp 6943-6946. doi: 10.1109/IGARSS.2018.8518533. 2018. Full-text
Kermit, Martin Andreas; Hamar, Jarle Bauck; Trier, Øivind Due. Towards a national infrastructure for semi-automatic mapping of cultural heritage in Norway. In: Oceans of data. Proceedings of the 44th Annual Conference on Computer Applications and Quantitative Methods in Archaeology. (ISBN 9781784917302). pp 159-172. 2018. Full-text
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