Examination of the effect of the basic parameters of the auto-encoder on coding performance
Küçük Resim Yok
Tarih
2017
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Ieee
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In this study, artificial learning approach which can express high dimensional data in a lower space (autocoding) and known as autoencoder in the literature has been investigated in detail without using a predefined ready mathematical model. The most important feature of this method, which can be used in place of traditional feature extraction methods (HOG, SHIFT, SURF, Wavelet, etc.), is the ability to extract data-specific features. By applying the real (MNIST) and synthetic data, the effects on the success of the parameters of the method are measured and the results are presented in a tabular form.
Açıklama
2017 International Artificial Intelligence and Data Processing Symposium (IDAP) -- SEP 16-17, 2017 -- Malatya, TURKEY
Anahtar Kelimeler
Deep learning, autoencoder, feature extraction, feature selection, data reduction, dimension reduction
Kaynak
2017 International Artificial Intelligence and Data Processing Symposium (Idap)
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
