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The importance of hyperparameters selection within small datasets

dc.contributor.authorAshrafi, Parivash
dc.contributor.authorSun, Yi
dc.contributor.authorDavey, Neil
dc.contributor.authorAdams, Roderick
dc.contributor.authorBrown, Marc
dc.contributor.authorPrapopoulou, Maria
dc.contributor.authorMoss, Gary
dc.date.accessioned2017-06-29T11:07:49Z
dc.date.available2017-06-29T11:07:49Z
dc.date.issued2015-10-01
dc.identifier.citationAshrafi , P , Sun , Y , Davey , N , Adams , R , Brown , M , Prapopoulou , M & Moss , G 2015 , The importance of hyperparameters selection within small datasets . in 2015 International Joint Conference on Neural Networks (IJCNN) . Institute of Electrical and Electronics Engineers (IEEE) , 2015 International Joint Conference on Neural Networks , Killarney , Ireland , 12/07/15 . https://doi.org/10.1109/IJCNN.2015.7280645
dc.identifier.citationconference
dc.identifier.isbn978-1-4799-1961-1
dc.identifier.isbn2161-4393
dc.identifier.isbn978-1-4799-1960-4
dc.identifier.urihttp://hdl.handle.net/2299/18647
dc.descriptionParivash Ashrafi, Yi Sun, Neil Davey, Rod Adams, Marc B. Brown, Maria Prapopoulou, and Gary Moss, 'The Importance of Hyperparameters Selection within Small Datasets', in Proceedings of the 2015 International Joint Conference on Neural Networks, published in IEEE Explore on 1 October 2015, DOI: 10.1109/IJCNN.2015.7280645. @2015 IEEE.
dc.description.abstractGaussian Process is a Machine Learning technique that has been applied to the analysis of percutaneous absorption of chemicals through human skin. The normal, automatic method of setting the hyperparameters associated with Gaussian Processes may not be suitable for small datasets. In this paper we investigate whether a handcrafted search method of determining these hyperparameters is better for such datasets.en
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof2015 International Joint Conference on Neural Networks (IJCNN)
dc.titleThe importance of hyperparameters selection within small datasetsen
dc.contributor.institutionSchool of Life and Medical Sciences
dc.contributor.institutionSchool of Computer Science
dc.contributor.institutionCentre for Computer Science and Informatics Research
dc.contributor.institutionDepartment of Pharmacy, Pharmacology and Postgraduate Medicine
dc.contributor.institutionCentre for Research into Topical Drug Delivery and Toxicology
dc.contributor.institutionPharmaceutics
dc.contributor.institutionSkin and Nail Group
dc.contributor.institutionAirway Group
dc.contributor.institutionBioadhesive Drug Delivery Group
dc.contributor.institutionNanopharmaceutics
dc.contributor.institutionPharmaceutical Analysis and Product Characterisation
dc.contributor.institutionToxicology
dc.contributor.institutionBiocomputation Research Group
dc.contributor.institutionDepartment of Computer Science
dc.contributor.institutionSchool of Physics, Engineering & Computer Science
rioxxterms.versionofrecord10.1109/IJCNN.2015.7280645
rioxxterms.typeOther
herts.preservation.rarelyaccessedtrue


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