Skip to main content
DeutschDE
European Commission logo print header

Learning from Massive, Incompletely annotated, and Structured Data

Projektbeschreibung


FET-Open Xtrack
The ultimate goal of the MAESTRA project is to develop predictive modelling methods capable of addressing massive sets of network data incompletely labelled with structured outputs.

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.The proposed project will develop predictive modelling methods capable of simultaneously addressing several (ultimately all) of the above complexity aspects. In the most complex case, the methods would be able to address massive sets of network data incompletely labelled with structured outputs. We will develop the foundations (basic concepts and notions) for and the methodology (design and implementation of algorithms) of such approaches. We will demonstrate the potential and utility of the methods on showcase problems from a diverse set of application areas (molecular biology, sensor networks, mutimedia, and social networks). Some of these applications, such as relating the composition of microbiota to human health and the design of social media aggregators, have the potential of transformational impact on important aspects of society, such as personalized medicine and social media.

Wissenschaftliches Gebiet

Programm/Programme

Thema/Themen

Aufforderung zur Vorschlagseinreichung

FP7-ICT-2013-X
Andere Projekte für diesen Aufruf anzeigen

Finanzierungsplan

CP - Collaborative project (generic)

Kontakt Koordinator

Saso DZEROVSKI Prof.

Koordinator

INSTITUT JOZEF STEFAN
EU-Beitrag
€ 597 375,00
Adresse
Jamova 39
1000 Ljubljana
Slowenien

Auf der Karte ansehen
Region
Slovenija Zahodna Slovenija Osrednjeslovenska
Aktivitätstyp
Research Organisations
Kontakt Verwaltung
Jadran Lenarcic (Prof.)
Links
Gesamtkosten
Keine Daten

Beteiligte (4)

Diese Seite teilen

Herunterladen

Letzte Aktualisierung: 20 November 2017

Mein Booklet

Permalink: https://cordis.europa.eu/project/id/612944/de

European Union, 2024