Author recognition by Abstract Feature Extraction
M Yasdi, B Diri - 2012 20th Signal Processing and …, 2012 - ieeexplore.ieee.org
M Yasdi, B Diri
2012 20th Signal Processing and Communications Applications …, 2012•ieeexplore.ieee.orgThe purpose of this study is to show the success of Abstract Feature Extraction Method in
multi dimensional feature vectors studies. Author recognition study is taken as an application
area and word root and 2 gram's are chosen as feature vectors. The success of the Abstract
Feature Extraction method in classification is shown on both Turkish and English data sets
by comparing with feature extraction methods such as PCA, CFS, chi-square.
multi dimensional feature vectors studies. Author recognition study is taken as an application
area and word root and 2 gram's are chosen as feature vectors. The success of the Abstract
Feature Extraction method in classification is shown on both Turkish and English data sets
by comparing with feature extraction methods such as PCA, CFS, chi-square.
The purpose of this study is to show the success of Abstract Feature Extraction Method in multi dimensional feature vectors studies. Author recognition study is taken as an application area and word root and 2 gram's are chosen as feature vectors. The success of the Abstract Feature Extraction method in classification is shown on both Turkish and English data sets by comparing with feature extraction methods such as PCA, CFS, chi-square.
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