Researcher:
Şen, Cansu

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Master Student

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Cansu

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Şen

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Şen, Cansu

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20152

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Researcher Of Publications: search.filters.isAuthorOfPublication.1490b322-0125-4906-8640-14894f6c2f64

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    SVM for sketch recognition: which hyperparameter interval to try ?
    (IEEE, 2015) Department of Computer Engineering; Sezgin, Tevfik Metin; Şen, Cansu; Yeşilbek, Kemal Tuğrul; Çakmak, Şerike; Faculty Member; Master Student; Master Student; Master Student; Department of Computer Engineering; College of Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; 18632; N/A; N/A; N/A
    Hyperparameters 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.
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    SVM for sketch recognition: which hyperparameter interval to try?
    (IEEE, 2015) N/A; N/A; N/A; Department of Computer Engineering; Yeşilbek, Kemal Tuğrul; Şen, Cansu; Çakmak, Şerike; Sezgin, Tevfik Metin; Master Student; Master Student; Master Student; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; N/A; 18632
    Hyperparameters 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.