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Learning from Massive, Incompletely annotated, and Structured Data – MAESTRA

Principal investigator

Category
FP
Area
CP
Department
ZEL
Start date
Feb 1st 2014
End date
Jan 31st 2017
Status
Done
Finance value
202320 EUR
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The need for machine learning (ML) and data mining (DM) is ever growing due to the increased pervasiveness of data analysis tasks in almost every area of life, including business, science and technology. Not only is the pervasiveness of data analysis tasks increasing, but so is their complexity. The proposed project will develop predictive modelling methods capable of simultaneously addressing several (ultimately all) complexity aspects. In the most complex case, the methods would be able to address massive sets of network data incompletely labelled with structured outputs.

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