MODE-CNN: A fast converging multi-objective optimization algorithm for CNN-based models
Yazarlar (4)
Doç. Dr. Özkan İNİK
Tokat Gaziosmanpaşa Üniversitesi, Türkiye
Arş. Gör. Mustafa ALTIOK
Tokat Gaziosmanpaşa Üniversitesi, Türkiye
Konya Teknik Üniversitesi, Türkiye
Türkiye
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale |
| Dergi Adı | Applied Soft Computing |
| Dergi ISSN | 1568-4946 Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Dergi Grubu | Q1 |
| Makale Dili | İngilizce |
| Basım Tarihi | 12-2021 |
| Cilt No | 109 |
| Sayı | 107582 |
| DOI Numarası | 10.1016/j.asoc.2021.107582 |
| Makale Linki | https://doi.org/10.1016/j.asoc.2021.107582 |
| Özet |
| Convolutional neural networks (CNNs) have been used to solve many problems in computer science with a high level of success, and have been applied in many fields in recent years. However, most of the designs of these models are still tuned manually; obtaining the highest performing CNN model is therefore very time-consuming, and is sometimes not achievable. Recently, researchers have started using optimization algorithms for the automatic adjustment of the hyper-parameters of CNNs. In particular, single-objective optimization algorithms have been used to achieve the highest network accuracy for the design of a CNN. When these studies are examined, it can be seen that the most significant problem in the optimization of the parameters of a CNN is that a great deal of time is required for tuning. Hence, optimization algorithms with high convergence rates are needed for the parameter optimization of deep … |
| Anahtar Kelimeler |
| Deep learning | CNN | Hyper-parameters optimization | Multi-objective | MODE |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| WoS | 18 |
| Google Scholar | 35 |