Department of Computer Engineering2024-11-0920159781-4673-7386-910.1109/SIU.2015.71299862-s2.0-84939126754http://dx.doi.org/10.1109/SIU.2015.7129986https://hdl.handle.net/20.500.14288/8803Hyperparameters are among the most crucial factors that effect the performance of machine learning algorithms. Since there is not a common ground on which hyperparameter combinations give the highest performance in terms of prediction accuracy, hyperparameter search needs to be conducted each time a model is to be trained. In this work, we analyzed how similar hyperparemeters perform on various datasets from sketch recognition domain. Results have shown that hyperparameter search space can be reduced to a subspace despite differences in dataset characteristics.Computer engineeringConference proceedinghttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84939126754anddoi=10.1109%2fSIU.2015.7129986andpartnerID=40andmd5=dd20468d2e5c38a58c10fc4e2b554b57N/A7748