Researcher:
Çakmak, Şerike

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Master Student

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Şerike

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Çakmak

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Çakmak, Şerike

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2015 - 20163

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Researcher Of Publications: search.filters.isAuthorOfPublication.07a0b643-0d3f-4011-9dce-80f7ed2bdb17

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Now showing 1 - 3 of 3
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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.
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    PublicationOpen Access
    Building a gold standard for perceptual sketch similarity
    (The Eurographics Association, 2016) Department of Computer Engineering; Sezgin, Tevfik Metin; Çakmak, Şerike; Faculty Member; Department of Computer Engineering; College of Engineering; Graduate School of Sciences and Engineering; 18632; N/A
    Similarity is among the most basic concepts studied in psychology. Yet, there is no unique way of assessing similarity of two objects. In the sketch recognition domain, many tasks such as classification, detection or clustering require measuring the level of similarity between sketches. In this paper, we propose a carefully designed experiment setup to construct a gold standard for measuring the similarity of sketches. Our setup is based on table scaling, and allows efficient construction of a measure of similarity for large datasets containing hundreds of sketches in reasonable time scales. We report the results of an experiment involving a total of 9 unique assessors, and 8 groups of sketches, each containing 300 drawings. The results show high interrater agreement between the assessors, which makes the constructed gold standard trustworthy.