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Comparison of Nonlinear Regression Models and Least Square Support Vector Machines for Egg Production Curve Fitting   
Yazarlar (2)
Prof. Dr. Özkan GÖRGÜLÜ Prof. Dr. Özkan GÖRGÜLÜ
Kırşehir Ahi Evran Üniversitesi, Türkiye
Dr. Öğr. Üyesi Aslı AKILLI Dr. Öğr. Üyesi Aslı AKILLI
Kırşehir Ahi Evran Üniversitesi, Türkiye
Devamını Göster
Özet
Egg production curves provide great benefits for decision makers on key issues such as the introduction of production patterns and animal breeding in poultry farming operations. Non-linear regression analysis methods have a wide range of applications in modeling time-varying data structures such as egg performance and egg weight. Kernel-based methods have been used as an alternative to nonlinear regression analysis, along with developing technology in recent years. The least squares support vector machines (LS-SVM) method is defined as a special type of kernel-based models and the modeling power of nonlinear regression analysis problems is quite successful. In this study, nonlinear regression analysis models and least squares support vector machines methods were examined comparatively for investigation of yield performance in laying hens. Adams-Bell, Compartmental, McNally, and Logistic …
Anahtar Kelimeler
Bildiri Türü Tebliğ/Bildiri
Bildiri Alt Türü Özet Metin Olarak Yayınlanan Tebliğ (Uluslararası Kongre/Sempozyum)
Bildiri Niteliği Alanında Hakemli Uluslararası Kongre/Sempozyum
Bildiri Dili İngilizce
Kongre Adı 3rd International Researchers, Statisticians and Young Statisticians Congress (IRSYSC-2017)
Kongre Tarihi 24-05-2017 / 26-05-2017
Basıldığı Ülke Türkiye
Basıldığı Şehir
BM Sürdürülebilir Kalkınma Amaçları
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