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dc.contributor.authorZhang, Jufenen_GB
dc.contributor.authorEverson, Richard M.en_GB
dc.date.accessioned2012-05-23T15:33:22Zen_GB
dc.date.accessioned2013-03-20T12:10:02Z
dc.date.issued2004en_GB
dc.description.abstractReasoning from data in practical problems is frequently hampered by missing observations. Mixture models provide a powerful general semi-parametric method for modelling densities and have close links to radial basis function neural networks (RBFs). We extend the Data Augmentation (DA) technique for multiple imputation to Gaussian mixture models to permit fully Bayesian inference of model parameters and estimation of the missing values. The method is compared to imputation using a single normal density on synthetic and real-world data. In addition to a lower mean squared error than can be achieved by simple imputation methods, mixture Models provide valuable information on the potentially multi-modal nature of imputed values. The DA formalism is extended to a classifier closely related to RBF networks permitting Bayesian classification with incomplete data; the technique is illustrated on synthetic and real datasets.en_GB
dc.identifier.citationpp. 296-303en_GB
dc.identifier.doi10.1109/ICMLA.2004.1383527en_GB
dc.identifier.urihttp://hdl.handle.net/10036/3539en_GB
dc.language.isoenen_GB
dc.publisherIEEEen_GB
dc.relation.urlhttp://dx.doi.org/10.1109/ICMLA.2004.1383527en_GB
dc.subjectnetworksen_GB
dc.titleBayesian estimation and classification with incomplete data using mixture modelsen_GB
dc.typeArticleen_GB
dc.date.available2012-05-23T15:33:22Zen_GB
dc.date.available2013-03-20T12:10:02Z
dc.contributor.editorKantardzic, Men_GB
dc.contributor.editorNasraoui, Oen_GB
dc.contributor.editorMilanova, Men_GB
dc.identifier.isbn07803-88232en_GB
dc.description©2004 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.en_GB
dc.identifier.journalProceedings of the 2004 International Conference on Machine Learning and Applicationsen_GB


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