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dc.contributor.author Wu, Wei
dc.contributor.author Shen, Qiang
dc.contributor.author Qu, Yanpeng
dc.contributor.author Mac Parthaláin, Neil
dc.date.accessioned 2010-09-23T08:50:12Z
dc.date.available 2010-09-23T08:50:12Z
dc.date.issued 2010-09-23
dc.identifier.citation Wu , W , Shen , Q , Qu , Y & Mac Parthaláin , N 2010 , ' Extreme Learning Machine for Mammographic Risk Analysis ' Proceedings of the 2010 UK Workshop on Computational Intelligence . en
dc.identifier.other PURE: 160141
dc.identifier.other dspace: 2160/5701
dc.identifier.uri http://hdl.handle.net/2160/5701
dc.description Y. Qu, Q. Shen, N. Mac Parthaláin and W. Wu. Extreme Learning Machine for Mammographic Risk Analysis. Proceedings of the 2010 UK Workshop on Computational Intelligence, 2010. en
dc.description.abstract The assessment of mammographic risk analysis is an important issue in the medical field. Various approaches have been applied in order to achieve a higher accuracy in such analysis. In this paper, an approach known as Extreme Learning Machines (ELM), is employed to generate a single hidden layer neural network based classifier for estimating mammographic risk. ELM is able to avoid problems such as local minima, improper learning rate, and overfitting which iterative learning methods tend to suffer from. In addition the training phase of ELM is very fast. The performance of the ELM-trained neural network is compared with a number of state of the art classifiers. The results indicate that the use of ELM entails better classification accuracy for the problem of mammographic risk analysis. en
dc.language.iso eng
dc.relation.ispartof Proceedings of the 2010 UK Workshop on Computational Intelligence en
dc.title Extreme Learning Machine for Mammographic Risk Analysis en
dc.type Text en
dc.type.publicationtype Article (Journal) en
dc.contributor.institution Department of Computer Science en
dc.description.status Peer reviewed en


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