A-DATADRIVE-B

Advanced Data-Driven Black-box modelling

 Coordinatore KATHOLIEKE UNIVERSITEIT LEUVEN 

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 Nazionalità Coordinatore Belgium [BE]
 Totale costo 2˙485˙800 €
 EC contributo 2˙485˙800 €
 Programma FP7-IDEAS-ERC
Specific programme: "Ideas" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
 Code Call ERC-2011-ADG_20110209
 Funding Scheme ERC-AG
 Anno di inizio 2012
 Periodo (anno-mese-giorno) 2012-04-01   -   2017-03-31

 Partecipanti

# participant  country  role  EC contrib. [€] 
1    KATHOLIEKE UNIVERSITEIT LEUVEN

 Organization address address: Oude Markt 13
city: LEUVEN
postcode: 3000

contact info
Titolo: Dr.
Nome: Stijn
Cognome: Delauré
Email: send email
Telefono: +32 16 320 944
Fax: +32 16 324 198

BE (LEUVEN) hostInstitution 2˙485˙800.00
2    KATHOLIEKE UNIVERSITEIT LEUVEN

 Organization address address: Oude Markt 13
city: LEUVEN
postcode: 3000

contact info
Titolo: Prof.
Nome: Johan Adelia K
Cognome: Suykens
Email: send email
Telefono: +32 16 321802
Fax: +32 16 321970

BE (LEUVEN) hostInstitution 2˙485˙800.00

Mappa


 Word cloud

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kernel    handling    representations    spaces    models    black    frameworks    optimization    data    box    tool    generation    candidate   

 Obiettivo del progetto (Objective)

'Making accurate predictions is a crucial factor in many systems (such as in modelling energy consumption, power load forecasting, traffic networks, process industry, environmental modelling, biomedicine, brain-machine interfaces) for cost savings, efficiency, health, safety and organizational purposes. In this proposal we aim at realizing a new generation of more advanced black-box modelling techniques for estimating predictive models from measured data. We will study different optimization modelling frameworks in order to obtain improved black-box modelling approaches. This will be done by specifying models through constrained optimization problems by studying different candidate core models (parametric models, support vector machines and kernel methods) together with additional sets of constraints and regularization mechanisms. Different candidate mathematical frameworks will be considered with models that possess primal and (Lagrange) dual model representations, functional analysis in reproducing kernel Hilbert spaces, operator splitting and optimization in Banach spaces. Several aspects that are relevant to black-box models will be studied including incorporation of prior knowledge, structured dynamical systems, tensorial data representations, interpretability and sparsity, and general purpose optimization algorithms. The methods should be suitable for handling larger data sets and high dimensional input spaces. The final goal is also to realize a next generation software tool (including symbolic generation of models and handling different supervised and unsupervised learning tasks, static and dynamic systems) that can be generically applied to data from different application areas. The proposal A-DATADRIVE-B aims at getting end-users connected to the more advanced methods through a user-friendly data-driven black-box modelling tool. The methods and tool will be tested in connection to several real-life applications.'

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