Skip to main content
Vai all'homepage della Commissione europea (si apre in una nuova finestra)
italiano it
CORDIS - Risultati della ricerca dell’UE
CORDIS
Contenuto archiviato il 2024-05-29

Scalable Online Learning Systems

Obiettivo

Learning to input data into a set of classes, given a set of classification examples, is the main subject to a multidisciplinary field of machine learning. The problem can be formulated as a minimization of a given cost function over possible classification functions. The properties of learning systems crucial for a successful application are: performance, i.e. high accuracy of classification, which depends on how well the used cost function corresponds to the application at hand, scalability, i.e ensuring that memory and time complexity of the learning grows gracefully with data size, and ability to process examples online as they come.

The objectives of this proposal are twofold. First, I intend to develop scalable systems that learn online and use structured (hence more natural) costs. These theoretical advances will facilitate development of learning-based systems for various applications. Second, I intend to apply these new learning methods in computer security (Intrusion Detection Systems) and bioinformatics (DNA splice site detection). To achieve the first goal, I will design new learning algorithms able to optimise structured costs common in non-Bayesian decision- making. I will build on recent methods from the Support Vector Machines learning, which transform the task to a Quadratic Programming (QP) optimisation.

The main idea of the proposed methodology is to exploit algorithms from computational geometry to derive online QP optimisation able to process large-scale data. A key to application of the pro posed algorithms to intrusion detection and splice site detection problems is understanding the problem-specific semantic constraints imposed by these applications. To achieve the second goal, I will incorporate the available semantic models into the learning algorithms, building on a large previous experience of the host institution (FhG-FIRST) in these respective problems.

Campo scientifico (EuroSciVoc)

Parole chiave

Argomento(i)

Invito a presentare proposte

FP6-2005-MOBILITY-5
Vedi altri progetti per questo bando

Meccanismo di finanziamento

EIF - Marie Curie actions-Intra-European Fellowships

Coordinatore

FRAUNHOFER-GESELLSCHAFT ZUR FORDERUNG DER ANGEWANDTEN FORSCHUNG E.V.
Contributo UE
Nessun dato
Costo totale
Nessun dato