Current Proceedings on Technology

Current Proceedings on Technology

A hybrid system via Support Vector Machine and Principal Component Analysis for Boreholes Soil Sample

Yazarlar: Ali Ulvi Uzer, Mustafa Serter Uzer

Cilt 5 , Sayı - , 2014 , Sayfalar -

Konular:-

Anahtar Kelimeler:Principle component analysis,Support vector machines,Soil sample,Konyaaltı

Özet: Data reduction is an important topic in the field of pattern recognition applications. The Principal Component Analysis (PCA) method is frequently used for data reduction. This study offers a hybrid approach that uses the PCA for data reduction and Support Vector Machines (SVM) for classification. In order to detect the accuracy of the suggested system, the soil samples taken from two boreholes was used. Classification accuracy of the proposed system reached 93.33% for soil sample dataset. The classification accuracy for this dataset were obtained through using ten-fold cross-validation method. As the results suggest, this system, which is performed through size reduction, is a feasible system for faster recognition of dataset so our study result appears to be very promising.

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@article{2014, title={A hybrid system via Support Vector Machine and Principal Component Analysis for Boreholes Soil Sample}, volume={5}, number={0}, publisher={Current Proceedings on Technology }, author={Ali Ulvi Uzer, Mustafa Serter Uzer}, year={2014} }
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Ali Ulvi Uzer, Mustafa Serter Uzer. (2014). A hybrid system via Support Vector Machine and Principal Component Analysis for Boreholes Soil Sample (Vol. 5). Vol. 5. Current Proceedings on Technology .
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Ali Ulvi Uzer, Mustafa Serter Uzer. A Hybrid System via Support Vector Machine and Principal Component Analysis for Boreholes Soil Sample. no. 0, Current Proceedings on Technology , 2014.
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