University of Hertfordshire

By the same authors

The importance of hyperparameters selection within small datasets

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Original languageEnglish
Title of host publication 2015 International Joint Conference on Neural Networks (IJCNN)
PublisherIEEE
ISBN (Electronic)978-1-4799-1960-4
ISBN (Print)978-1-4799-1961-1, 2161-4393
DOIs
StatePublished - 1 Oct 2015
Event2015 International Joint Conference on Neural Networks - Killarney, Ireland

Conference

Conference2015 International Joint Conference on Neural Networks
Abbreviated titleIJCNN
CountryIreland
CityKillarney
Period12/07/1517/07/15

Abstract

Gaussian 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.

Notes

Parivash 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.

ID: 11097734