Our fieldMachine learning is a vibrant and rapidly developing field at the intersection of artificial intelligence, computer science and statistics. Machine learning was originally defined as data-driven learning of automatic prediction and decision systems. The field has expanded substantially in recent years to also encompass problems in the natural and social sciences, and the term is now often used for any problem involving learning and prediction in complex data problems.
Our focusThe members of the machine learning research group at IDA are active in both methodological and applied research in several of the main subfields of machine learning:
New publicationsLearning AMP Chain Graphs and some Marginal Models Thereof under Faithfulness
International Journal of Approximate Reasoning.
BROCCOLI: Software for Fast fMRI Analysis on Many-Core CPUs and GPUs
Frontiers in Neuroinformatics
Bootstrap Confidence Intervals for Large-Scale Multivariate Monotonic Regression Problems
Communications in Statistics - Simulation and Computation
A Biologically Based Model for Recognition of 2-D Occluded Patterns
Probabilistic Analysis of Power and Temperature Under Process Variation for Electronic System Design
IEEE Transactions on Computer-aided Design of Integrated Circuits and Systems
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Page responsible: Fredrik Lindsten
Last updated: 2014-06-08