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Journals / Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi / 2014 / Cilt: 9 - Sayı: 1

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

Destek Vektör Makineleriyle Sınıflandırma Problemlerinin Çözümü İçin Çekirdek Fonksiyonu Seçimi

Sevgi AYHAN
Şenol ERDOĞMUŞ
Pages
175–201
Publication Date
2014-11-02
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

As a result, the research proved that radial based Kernel function was the most successful Kernel function was proved.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:One of the most important machine learning algorithms developed for to accomplish classification task of data mining is Support Vector Machines.
📊
Key Findings & Data:In the literature, Support Vector Machines has been shown to outperform many other techniques.
💡
Conclusion & Impact:As a result, the research proved that radial based Kernel function was the most successful Kernel function was proved.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Veri madenciliği · Destek vektör makineleri · Çekirdek fonksiyonu seçimi

Abstract

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.

Özet

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.

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Cite This Article

AYHAN, S., ERDOĞMUŞ, Ş. (2014). Kernel Function Selection for the Solution of Classification Problems via Support Vector Machines. Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi, Cilt: 9 - Sayı: 1, 175–201. https://ajindex.com/articles/30608

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