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. Fields of science natural sciencescomputer and information sciencesartificial intelligencemachine learningunsupervised learningnatural sciencesmathematicsapplied mathematicsdynamical systems Programme(s) 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) Topic(s) ERC-AG-PE7 - ERC Advanced Grant - Systems and communication engineering Call for proposal ERC-2011-ADG_20110209 See other projects for this call Funding Scheme ERC-AG - ERC Advanced Grant Coordinator KATHOLIEKE UNIVERSITEIT LEUVEN Address Oude markt 13 3000 Leuven Belgium See on map Region Vlaams Gewest Prov. Vlaams-Brabant Arr. Leuven Activity type Higher or Secondary Education Establishments Principal investigator Johan Adelia K Suykens (Prof.) Administrative Contact Stijn Delauré (Dr.) Links Contact the organisation Opens in new window Website Opens in new window EU contribution No data Beneficiaries (1) Sort alphabetically Sort by EU Contribution Expand all Collapse all KATHOLIEKE UNIVERSITEIT LEUVEN Belgium EU contribution € 2 485 800,00 Address Oude markt 13 3000 Leuven See on map Region Vlaams Gewest Prov. Vlaams-Brabant Arr. Leuven Activity type Higher or Secondary Education Establishments Principal investigator Johan Adelia K Suykens (Prof.) Administrative Contact Stijn Delauré (Dr.) Links Contact the organisation Opens in new window Website Opens in new window Other funding No data
