Research on Inductive Logic Program Design and Learning Algorithm Based on Higher Order Logic and Its Application View Homepage


Ontology type: schema:MonetaryGrant     


Grant Info

YEARS

2013-2016

FUNDING AMOUNT

250000 CNY

ABSTRACT

By using first-order logic to represent empirical data and learned rules, Inductive logic programming (ILP for short) overcomes two limitations of classical machine learning: a limited knowledge representation formalism which is essentially propositional logic and inability to use substantial background knowledge in the learning process. In recent years, the first-class international journal "machine learning" has published several special issues on ILP. ILP has been a hot topic of machine learning. The research further improves the expressive ability of ILP, adopts higher-order logic to represent empirical data and learned rules, and studies higher-order logic based ILP learning algorithm and its application. Firstly, according to search strategy, determined ILP algorithm and stochastic ILP algorithm are studied. Secondly, in practical applications, unlabeled data are readily available but labeled data are fairly expensive to obtain because they require human effort. Based on the two algorithms proposed above, the project investigates how to exploit unlabeled data to enhance classification performance of higher-order ILP algorithm. Finally, higher-order ILP algorithm is applied to image semantic classification system adopting higher-order logic to represent space relation. This research provides machine learnin More... »

URL

http://npd.nsfc.gov.cn/projectDetail.action?pid=61300098

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