The importance of hyperparameters selection within small datasets
Ashrafi, Parivash, Sun, Yi, Davey, Neil, Adams, Roderick, Brown, Marc, Prapopoulou, Maria and Moss, Gary
(2015)
The importance of hyperparameters selection within small datasets.
Institute of Electrical and Electronics Engineers (IEEE).
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.
| Item Type | Other |
|---|---|
| Divisions |
?? sbu_slms ?? ?? sbu_scs ?? ?? rc_csir ?? ?? dep_pppm ?? ?? rc_tddt ?? ?? rg_pharma ?? ?? rg_sng ?? ?? rg_ag ?? ?? rg_bddg ?? ?? rg_nano ?? ?? rg_papc ?? ?? rg_tox ?? ?? rg_bio_comp ?? |
| Date Deposited | 18 Nov 2024 12:20 |
| Last Modified | 18 Nov 2024 12:20 |