All Outputs (2)

Twitter response to televised political debates in Election 2015. (2015)
Book Chapter
PEDERSEN, S., BAXTER, G., BURNETT, S., MACLEOD, I., GOKER, A., HERON, M., ISAACS, J., ELYAN, E. and KALICIAK, L. 2015. Twitter response to televised political debates in Election 2015. In Jackson, D. and Thorsen, E. (eds.) UK election analysis 2015: media, voters and the campaign: early reflections from leading UK academics. Poole: Bournemouth University, centre for the study of journalism, culture and community [online], page 73. Available from: http://www.electionanalysis.uk/uk-election-analysis-2015/section-6-social-media/twitter-response-to-televised-political-debates-in-election-2015/

The advent of social media such as Twitter has revolutionised our conversations about live television events. In the days before the Internet, conversation about television programmes was limited to those sitting on the sofa with you and people you m... Read More about Twitter response to televised political debates in Election 2015..

A scalable expressive ensemble learning using random prism: a MapReduce approach. (2015)
Book Chapter
STAHL, F., MAY, D., MILLS, H., BRAMER, M. and GABER, M.M. 2015. A scalable expressive ensemble learning using random prism: a MapReduce approach. In Hameurlain, A., Küng, J., Wagner, R., Sakr, S., Wang, L. and Zomaya, A. (eds.) Transactions on large-scale data- and knowledge-centred systems XX: special issue on advanced techniques for big data management. Lecture notes in computer science, 9070. Berlin: Springer [online], pages 90-107. Available from: https://doi.org/10.1007/978-3-662-46703-9_4

The induction of classification rules from previously unseen examples is one of the most important data mining tasks in science as well as commercial applications. In order to reduce the influence of noise in the data, ensemble learners are often app... Read More about A scalable expressive ensemble learning using random prism: a MapReduce approach..