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Destek Vektör Makineleriyle Sınıflandırma Problemlerinin Çözümü için Çekirdek Fonksiyonu Seçimi

Kernel Function Selection for the Solution of Classification Problems via Support Vector Machines

Journal Name:

  • Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi

Publication Year:

  • 2014

Key Words:

Keywords (Original Language):

Author NameUniversity of AuthorFaculty of Author
Abstract (2. Language): 
One of the most important machine learning algorithms developed for to accomplish classification task of data mining is Support Vector Machines. In the literature, Support Vector Machines has been shown to outperform many other techniques. Kernel function selection and parameter optimization play important role in implementation of Support Vector Machines. In this study, Kernel function selection process was ground on the randomized block experimental design. Univariate ANOVA was utilized for kernel function selection. As a result, the research proved that radial based Kernel function was the most successful Kernel function was proved.
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Abstract (Original Language): 
Veri madenciliğinin görevlerinden biri olan sınıflandırma probleminin çözümü için geliştirilmiş önemli makine öğrenimi algoritmalarından biri Destek Vektör Makineleri’dir. Literatürde Destek Vektör Makineleri’nin diğer birçok tekniğe göre daha başarılı sonuçlar verdiği kanıtlanmıştır. Destek Vektör Makineleri’nin uygulanması sürecinde çekirdek fonksiyonu seçimi ve parametre optimizasyonu önemli rol oynamaktadır. Bu çalışmada, çekirdek fonksiyonu seçim süreci rassal blok deney tasarımı temeline oturtulmuştur. Çekirdek fonksiyonun seçiminde tek değişkenli varyans analizinden (Univariate ANOVA) yararlanılmıştır. Sonuç olarak en başarılı performansa sahip çekirdek fonksiyonunun radyal tabanlı fonksiyon olduğu kanıtlanmıştır.
FULL TEXT (PDF): 
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  • Turkish

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