Deep Learning(DL) algotithms have become popular with the detailed analyzing capabilities with many hidden layers in recent years. The size of hidden layer in the classifier models is complately correlated with the analyzing capability of the proposed mode. Multiple hidden layers and neuron size in the hidden layers enhance the analyzing capability of the models,whereas increasing the training time.
Eser Adı (dc.title) | Deep Learning for COPD Analysis Using Lung Sounds |
Yayın Türü (dc.type) | Konferans Bildirisi |
Yazar/lar (dc.contributor.author) | ALLAHVERDİ, Novruz |
Yazar/lar (dc.contributor.author) | ALTAN, Gökhan |
Yazar/lar (dc.contributor.author) | KUTLU, Yakup |
Atıf Dizini (dc.source.database) | Diğer |
Konu Başlıkları (dc.subject) | Deep Learning |
Konu Başlıkları (dc.subject) | Deep Belief Networks |
Konu Başlıkları (dc.subject) | Hibert-Huang Transform |
Yayın Tarihi (dc.date.issued) | 2018 |
Kayıt Giriş Tarihi (dc.date.accessioned) | 2019-07-10T08:12:01Z |
Açık Erişim tarihi (dc.date.available) | 2019-07-10T08:12:01Z |
Özet (dc.description.abstract) | Deep Learning(DL) algotithms have become popular with the detailed analyzing capabilities with many hidden layers in recent years. The size of hidden layer in the classifier models is complately correlated with the analyzing capability of the proposed mode. Multiple hidden layers and neuron size in the hidden layers enhance the analyzing capability of the models,whereas increasing the training time. |
Yayın Dili (dc.language.iso) | en |
Tek Biçim Adres (dc.identifier.uri) | https://hdl.handle.net/20.500.12498/1026 |