Hyperparameters are among the most crucial factors that affect the performance of machine learning algorithms. In general, there is no direct method for determining a set of satisfactory parameters, so hyperparameter search needs to be conducted each time a model is to be trained. In this work, we analyze how similar hyperparameters perform across various datasets from the sketch recognition domain. Results show that hyperparameter search space can be reduced to a subspace despite differences in characteristics of datasets.
Authors: Kemal Tugrul Yesilbek, Cansu Sen, Serike Cakmak and T. Metin Sezgin has been accepted for publication in Expressive 2015.