Araştırma Çıktıları | WoS | Scopus | TR-Dizin | PubMed

Permanent URI for this communityhttps://hdl.handle.net/20.500.14719/1741

Browse

Filters

2012 - 20143

Settings

Start date: 2010End date: 2019Type: search.filters.itemtype.Conference PaperSubject: search.filters.subject.Signal ProcessingAuthor: search.filters.author.Kursun, Olcay

Search Results

Now showing 1 - 3 of 3
  • No Thumbnail Available
    PublicationMetadata only
    Feature extraction for facial expression recognition by canonical correlation analysis, Kanoni̇k korelasyon anali̇zi̇ i̇le yüz i̇fadesi̇nden duygu tanima i̇çi̇n özni̇teli̇ k çikarimi
    (2012) Sakar, C. Okan; Kursun, Olcay; Karaali, Ali; Erdem, Cigdem Eroglu; Sakar, C. Okan, Bahçeşehir Üniversitesi, Istanbul, Turkey; Kursun, Olcay, Istanbul Üniversitesi, Istanbul, Turkey; Karaali, Ali, Bahçeşehir Üniversitesi, Istanbul, Turkey; Erdem, Cigdem Eroglu, Bahçeşehir Üniversitesi, Istanbul, Turkey
    Although several methods have been proposed for fusing different image representations obtained by different preprocessing methods for emotion recognition from the facial expression in a given image, the dependencies and relations among them have not been much investigated. In this study, it has been shown that covariates obtained by Canonical Correlation Analysis (CCA) that extracts relations between different representations have high predictive power for emotion recognition. As high prediction accuracy can be achieved using a small number of features extracted by it, CCA is considered to be a good dimensionality reduction method. For our simulations, we used the CK+ database and showed that covariates obtained from difference-images and geometric-features representations have high prediction accuracy. © 2012 IEEE. © 2012 Elsevier B.V., All rights reserved.
  • No Thumbnail Available
    PublicationMetadata only
    A validation method for comparing classifiers on imbalanced datasets, Dengesi̇z veri̇ kümeleri̇ üzeri̇nde siniflandiricilarin karşilaştirilmasi i̇çi̇n bi̇r sinama yöntemi̇
    (2012) Erdogdu Sakar, Betul; Sakar, C. Okan; Gürgen, Fïkret S.; Sertbaş, Ahmet; Kursun, Olcay; Erdogdu Sakar, Betul, Bahçeşehir Üniversitesi, Istanbul, Turkey; Sakar, C. Okan, Bahçeşehir Üniversitesi, Istanbul, Turkey; Gürgen, Fïkret S., Boğaziçi Üniversitesi, Bebek, Turkey; Sertbaş, Ahmet, Istanbul Üniversitesi, Istanbul, Turkey; Kursun, Olcay, Istanbul Üniversitesi, Istanbul, Turkey
    In this study, to compare the robustness and learning capability of the classifiers on imbalanced datasets, a cross validation method that generates class-imbalanced training sets is proposed. The method will also be used to evaluate the accuracies of methods developed for dealing with the class-imbalance problem. The proposed method is used to generate imbalanced datasets from three biomedical datasets. Then, k-Nearest Neighbor, Support Vector Machines and Multi Layer Perceptron classifiers are compared using various settings of their hyper-parameters that affect their complexities. The experimental results show that SVMs are simply the most robust of all when applied to imbalanced datasets. © 2012 IEEE. © 2012 Elsevier B.V., All rights reserved.
  • No Thumbnail Available
    PublicationMetadata only
    Prediction of level and abrupt changes of ozon concentration, Ozon seviyesi ve ani deǧişimlerinin kestirimi
    (IEEE Computer Society [email protected], 2014) Develi, Ahmet; Kursun, Olcay; Erdogdu Sakar, Betul; Develi, Ahmet, Istanbul Üniversitesi, Istanbul, Turkey; Kursun, Olcay, Istanbul Üniversitesi, Istanbul, Turkey; Erdogdu Sakar, Betul, Bahçeşehir Üniversitesi, Istanbul, Turkey
    While, in stratosphere, high level ozone concentration protects the Earth against ultraviolet radiation, in lower troposphere it has negative effects on human health and environment. The goal of this study is to determine the feature groups that are related to abrupt changes in the level of ozone. Linear discriminant analysis and support vector machines methods are used to explore which combination of features are predictive of abrupt changes in ozone level on the simulation dataset collected in Ankara, Turkey, by an automatic air quality monitoring station operated by the ministry of environment and urban planning. The dataset consists of one year of measurements of air pollutants and the meteorological factors. The obtained results showed that particulate matters, nitric oxides and temperature are most effective parameters in the classification of absurt rise and fall in the level of ozone. © 2014 IEEE. © 2014 Elsevier B.V., All rights reserved.