Incorporating risk assessment constraints into machine learning techniques: improved prediction models View Homepage


Ontology type: schema:MonetaryGrant     


Grant Info

YEARS

2010-2011

FUNDING AMOUNT

29700 EUR

ABSTRACT

Models are pervasive in science and are widely used in almost every area of business to support decision making and improve human performance. Machine learning has made significant advances in abilities to construct realistic models, but the standard learning algorithms are not equipped to handle domain constraints of risk assessment. The aim of this post-doctoral project is to study performance measures in order to understand their relations and use them as a way to incorporate learning task-specific constraints into machine learning techniques. The approach will be a mixture of machine learning theory, systematic study of the state-of-the-art, algorithm design and testing with experimental data. The project will lead to better machine learning algorithms, prediction models, and decision-making tools as well as an increased understanding of the gamut of measures. More... »

URL

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