Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/23700
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dc.contributorSchool of Hotel and Tourism Managementen_US
dc.creatorWu, Qen_US
dc.creatorLaw, Ren_US
dc.creatorXu, Xen_US
dc.date.accessioned2015-05-26T08:13:33Z-
dc.date.available2015-05-26T08:13:33Z-
dc.identifier.issn0957-4174en_US
dc.identifier.urihttp://hdl.handle.net/10397/23700-
dc.language.isoenen_US
dc.publisherPergamon Pressen_US
dc.rights© 2011 Elsevier Ltd. All rights reserveden_US
dc.rights© 2011. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.subjectSparse Gaussian processen_US
dc.subjectSupport vector machineen_US
dc.subjectTourism demand forecastingen_US
dc.subjectKernel machinesen_US
dc.titleA sparse gaussian process regression model for tourism demand forecasting in Hong Kongen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage4769en_US
dc.identifier.epage4774en_US
dc.identifier.volume39en_US
dc.identifier.issue5en_US
dc.identifier.doi10.1016/j.eswa.2011.09.159en_US
dcterms.abstractIn recent years, Gaussian process (GP) models have been popularly studied to solve hard machine learning problems. The models are important due to their flexible non-parametric modeling abilities using Mercer kernels and the Bayesian framework for probabilistic inference. In this paper, we propose a sparse GP regression (GPR) model for tourism demand forecasting in Hong Kong. The sparsification procedure of the GPR model not only decreases the computational complexity but also improves the generalization ability. We experiment the proposed model with monthly demand data that are relevant to Hong Kong's tourism industry, and compare the performance of the sparse GPR model with those of various kernel-based models to show its effectiveness. The proposed sparse GPR model shows that its forecasting capability outperforms those of the ARMA model and the two state-of-the-art SVM models.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationExpert systems with applications, Apr. 2012, v. 39, no. 5, p. 4769-4774en_US
dcterms.isPartOfExpert systems with applicationsen_US
dcterms.issued2012-04-
dc.identifier.isiWOS:000301155300012-
dc.identifier.eissn1873-6793en_US
dc.identifier.rosgroupidr61228-
dc.description.ros2011-2012 > Academic research: refereed > Publication in refereed journalen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumbera0635-n05-
dc.description.fundingSourceRGCen_US
dc.description.fundingTextGRF: 15503814en_US
dc.description.pubStatusPublisheden_US
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