dc.contributor.author | Fieldsend, Jonathan E. | |
dc.date.accessioned | 2013-07-09T09:22:31Z | |
dc.date.issued | 2006-02-10 | |
dc.description.abstract | In this chapter recent research in the area of multi-objective optimisation of regression models is presented and combined. Evolutionary multi-objective optimisation techniques are described for training a population of regression models to optimise the recently defined Regression Error Characteristic Curves (REC). A method which meaningfully compares across regressors and against benchmark models (i.e. ‘random walk’ and maximum a posteriori approaches) for varying error rates. Through bootstrapping training data, degrees of confident out-performance are also highlighted. | en_GB |
dc.identifier.citation | In: Multi-Objective Machine Learning, edited by Yaochu Jin, pp. 103 - 123. Studies in Computational Intelligence, vol 16 | en_GB |
dc.identifier.doi | 10.1007/3-540-33019-4_5 | |
dc.identifier.uri | http://hdl.handle.net/10871/11583 | |
dc.language.iso | en | en_GB |
dc.publisher | Springer | en_GB |
dc.title | Regression Error Characteristic Optimisation of Non-Linear Models | en_GB |
dc.type | Book chapter | en_GB |
dc.date.available | 2013-07-09T09:22:31Z | |
dc.contributor.editor | Jin, Y | |
dc.identifier.isbn | 9783540306764 | |
dc.identifier.isbn | 9783540330196 | |
dc.identifier.issn | 1860-949X | |
dc.relation.isPartOf | Multi-Objective Machine Learning | |
dc.description | Copyright © 2006 Springer-Verlag Berlin Heidelberg. The final publication is available at link.springer.com | en_GB |
dc.relation.isPartOfSeries | Studies in Computational Intelligence | |