Welcome to MAESTRA project

Welcome to MAESTRA project Learning from Massive, Incompletely annotated, and Structured Data 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. We are increasingly often facing predictive modelling tasks involving one or several of the following complexity aspects: (a) structured data as input or output of the prediction process, (b) very large/massive datasets, with many examples and/or many input/output dimensions, where data may be streaming at high rates, (c) incompletely/partially labelled data, and (d) data placed in a spatio-temporal or network context. Each of these is a major challenge to current ML/DM approaches and is the central topic of active research in areas such as structured-output prediction, mining data streams, semi-supervised learning, and mining network data. The simultaneous presence of several of them is a much harder, currently insurmountable, challenge and severely limits the applicability of ML/DM approaches. ...

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Events

ACM Symposium on Applied Computing Data Streams Track

ACM Symposium on Applied Computing Data Streams Track

ACM Symposium on Applied Computing Data Streams Track in conjunction with ACM Symposium on Applied C...
International workshop – Machine Learning and Systems Biosciences

International workshop – Machine Learning and Systems Biosciences

In the frame of the EU project "Internacionalizacija visokega šolstva"...

Best paper at IDEAL 2015

Best student paper at Discovery Science 2015

MAESTRA paper published in Information Sciences