Destek vektör makinalari model parametrelerinin yüksek boyutlu model gösterilimi ile optimizasyonu ve hiperspektral görüntü lere uygulanmasi

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5 Atıf (Scopus)

Özet

Support vector machines (SVM) is one of the most important methods which has been frequently used in classification of remote sensing images. The classification performance of the SVM strictly depends on choice of convenient kernel function and its kernel parameters called model selection. In the case that the parameters are not appropriately chosen, SVM may result in relatively poor performance. Therefore, the choice of suitable kernel and its parameters is an important topic in classification problems. In this paper, we studied on the optimal selection of the radial basis kernel parameters of SVM using High Dimensional Model Representation (HDMR) which was recently proposed as an efficient tool to capture the input-output relationships in high-dimensional systems for many problems in science and engineering. The performance of the proposed approach was first analysed with some mathematical functions whose optimums are analytically known in comparison to the grid search method. Different experiments were also conducted with synthetic and hyperspectral datasets. The main advantage of the approach over the grid-search is to require relatively few number of training evaluation and hence less computational time in order to optimize the parameters. Therefore, training time required for SVM is significantly reduced.

Tercüme edilen katkı başlığıOptimization of SVM parameters using High Dimensional Model Representation and its application to hyperspectral images
Orijinal dilTürkçe
Ana bilgisayar yayını başlığı2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Proceedings
YayınlayanIEEE Computer Society
Sayfalar642-645
Sayfa sayısı4
ISBN (Basılı)9781479948741
DOI'lar
Yayın durumuYayınlandı - 2014
Etkinlik2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Trabzon, Turkey
Süre: 23 Nis 201425 Nis 2014

Yayın serisi

Adı2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Proceedings

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???event.eventtypes.event.conference???2014 22nd Signal Processing and Communications Applications Conference, SIU 2014
Ülke/BölgeTurkey
ŞehirTrabzon
Periyot23/04/1425/04/14

Keywords

  • high dimensional model representation
  • Model selection
  • optimization
  • support vector machines

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