The importance of hyperparameters selection within small datasets

Parivash Ashrafi; Yi Sun; Neil Davey; Roderick Adams; Marc Brown; Maria  Prapopoulou; Gary Moss

doi:10.1109/IJCNN.2015.7280645

The importance of hyperparameters selection within small datasets

Parivash Ashrafi, Yi Sun, Neil Davey, Roderick Adams, Marc Brown, Maria Prapopoulou, Gary Moss

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

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.

Original language	English
Title of host publication	2015 International Joint Conference on Neural Networks (IJCNN)
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
ISBN (Electronic)	978-1-4799-1960-4
ISBN (Print)	978-1-4799-1961-1, 2161-4393
DOIs	https://doi.org/10.1109/IJCNN.2015.7280645
Publication status	Published - 1 Oct 2015
Event	2015 International Joint Conference on Neural Networks - Killarney, Ireland Duration: 12 Jul 2015 → 17 Jul 2015

Conference

Conference	2015 International Joint Conference on Neural Networks
Abbreviated title	IJCNN
Country/Territory	Ireland
City	Killarney
Period	12/07/15 → 17/07/15

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10.1109/IJCNN.2015.7280645

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@inproceedings{07fdda3e90de403e8d9a85e4f8de803b,

title = "The importance of hyperparameters selection within small datasets",

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.",

author = "Parivash Ashrafi and Yi Sun and Neil Davey and Roderick Adams and Marc Brown and Maria Prapopoulou and Gary Moss",

note = "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.; 2015 International Joint Conference on Neural Networks , IJCNN ; Conference date: 12-07-2015 Through 17-07-2015",

year = "2015",

month = oct,

day = "1",

doi = "10.1109/IJCNN.2015.7280645",

language = "English",

isbn = "978-1-4799-1961-1",

booktitle = "2015 International Joint Conference on Neural Networks (IJCNN)",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

address = "United States",

}

Ashrafi, 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

TY - GEN

T1 - The importance of hyperparameters selection within small datasets

AU - Ashrafi, Parivash

AU - Sun, Yi

AU - Davey, Neil

AU - Adams, Roderick

AU - Brown, Marc

AU - Prapopoulou, Maria

AU - Moss, Gary

N1 - 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.

PY - 2015/10/1

Y1 - 2015/10/1

N2 - 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.

AB - 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.

U2 - 10.1109/IJCNN.2015.7280645

DO - 10.1109/IJCNN.2015.7280645

M3 - Conference contribution

SN - 978-1-4799-1961-1

SN - 2161-4393

BT - 2015 International Joint Conference on Neural Networks (IJCNN)

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - 2015 International Joint Conference on Neural Networks

Y2 - 12 July 2015 through 17 July 2015

ER -

The importance of hyperparameters selection within small datasets

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