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Destek Vektör Makineleriyle Sınıflandırma Problemlerinin Çözümü İçin Çekirdek Fonksiyonu Seçimi

Year 2014, Volume 9, Issue 1, 175 - 201, 02.11.2014

Abstract

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.

References

  • Burges, C. J. C. (1998). A tutorial on support vector machines for pattern recognition, data mining and knowledge discovery. Kluwer Academic Publishers, 2 (2), 121-1
  • Busuttil, S. (2003). Support vector machines. In Proceedings of the Computer Science Annual Research Workshop, Villa Bighi, Kalkara, University of Malta.
  • Conagin, A., Barbin, D., Demétrio, C.G.B. (2008). Modifications for the Tukey test procedure and evaluation of the power and efficiency of multiple comparison procedures. Scientia Agricola, 65, 428-432.
  • Cortes, C., Vapnik, V. (1995). Support vector networks, Machine Learning, 20,1-25.
  • Çakar, Ö. (2007). Fonksiyonel analize giriş I. A.Ü. Fen Fakültesi Döner Sermaye
  • İşletmesi Yayınları, no:13, (Erwin KREYSZİG’den Uyarlama). Demirci, D. A. (2007). Destek vektör makineleri ile karakter tanıma, Yıldız Teknik
  • Üniversitesi, Fen Bilimleri Enstitüsü, Yüksek Lisans Tezi, İstanbul. Fernandez, G.C.J. (1992). Residual analysis and data transformations: Important tools in statistical analysis. HortScience, 27, 297–300.
  • Fletcher, T. (2009). Support vector machines explained. www.cs.ucl.ac.uk/sta_/T.Fletcher/
  • Gunn, S. R. (1998). Support vector machines for classification and regression.
  • Technical Report, Faculty of Engineering, Science and Mathematics, School of Electronics and Computer Science. http://users.ecs.soton.ac.uk/srg/publications/pdf/SVM.pdf Huang, C. L. and Wang, C. J. (2006). A GA-based feature selection and parameter optimization for support vector machines. Expert Systems with Applications, 31:231-240.
  • Katagiri, S. and Abe, S. (2006). Incremental training of support vector machines using hyperspheres. Pattern Recognition Letters, 27 (13), 1495-1507
  • Li, S., Li, H., Li, M., Shyr, Y., Xie, L. and Li, Y. (2009). Improved prediction of lysine acetylation by support vector machines. Protein and peptide letters, 16, 977-983.
  • Nitze, I., Schulthess, U. And Asche, H. (2012). Comparison of machine learning algorithms random forest, artficial neural network and support vector machine to maximum likelihood for supervised crop type classification. Proceedings of the 4th
  • GEOBIA, Janeiro - Brazil., 35-40.

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

Year 2014, Volume 9, Issue 1, 175 - 201, 02.11.2014

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.

References

  • Burges, C. J. C. (1998). A tutorial on support vector machines for pattern recognition, data mining and knowledge discovery. Kluwer Academic Publishers, 2 (2), 121-1
  • Busuttil, S. (2003). Support vector machines. In Proceedings of the Computer Science Annual Research Workshop, Villa Bighi, Kalkara, University of Malta.
  • Conagin, A., Barbin, D., Demétrio, C.G.B. (2008). Modifications for the Tukey test procedure and evaluation of the power and efficiency of multiple comparison procedures. Scientia Agricola, 65, 428-432.
  • Cortes, C., Vapnik, V. (1995). Support vector networks, Machine Learning, 20,1-25.
  • Çakar, Ö. (2007). Fonksiyonel analize giriş I. A.Ü. Fen Fakültesi Döner Sermaye
  • İşletmesi Yayınları, no:13, (Erwin KREYSZİG’den Uyarlama). Demirci, D. A. (2007). Destek vektör makineleri ile karakter tanıma, Yıldız Teknik
  • Üniversitesi, Fen Bilimleri Enstitüsü, Yüksek Lisans Tezi, İstanbul. Fernandez, G.C.J. (1992). Residual analysis and data transformations: Important tools in statistical analysis. HortScience, 27, 297–300.
  • Fletcher, T. (2009). Support vector machines explained. www.cs.ucl.ac.uk/sta_/T.Fletcher/
  • Gunn, S. R. (1998). Support vector machines for classification and regression.
  • Technical Report, Faculty of Engineering, Science and Mathematics, School of Electronics and Computer Science. http://users.ecs.soton.ac.uk/srg/publications/pdf/SVM.pdf Huang, C. L. and Wang, C. J. (2006). A GA-based feature selection and parameter optimization for support vector machines. Expert Systems with Applications, 31:231-240.
  • Katagiri, S. and Abe, S. (2006). Incremental training of support vector machines using hyperspheres. Pattern Recognition Letters, 27 (13), 1495-1507
  • Li, S., Li, H., Li, M., Shyr, Y., Xie, L. and Li, Y. (2009). Improved prediction of lysine acetylation by support vector machines. Protein and peptide letters, 16, 977-983.
  • Nitze, I., Schulthess, U. And Asche, H. (2012). Comparison of machine learning algorithms random forest, artficial neural network and support vector machine to maximum likelihood for supervised crop type classification. Proceedings of the 4th
  • GEOBIA, Janeiro - Brazil., 35-40.

Details

Primary Language Turkish
Subjects Social
Journal Section Articles
Authors

Sevgi AYHAN This is me


Şenol ERDOĞMUŞ This is me

Publication Date November 2, 2014
Application Date November 2, 2014
Acceptance Date
Published in Issue Year 2014, Volume 9, Issue 1

Cite

APA Ayhan, S. & Erdoğmuş, Ş. (2014). Destek Vektör Makineleriyle Sınıflandırma Problemlerinin Çözümü İçin Çekirdek Fonksiyonu Seçimi . Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi , 9 (1) , 175-201 . Retrieved from https://dergipark.org.tr/en/pub/oguiibf/issue/5712/76473