Initialization and training improvements for conic section function neural networks
Bu tez size mi ait?
Bu kayıt toplu arşivden geldi. Sizinse profilinize bağlayın.
Özet (EN)
In this study, Genetic Algorithm is used to determine the center vectors of Conic Section Function Neural Networks on initialization phase and improved cone folding method is proposed for training phase of Conic Section Function Neural Networks. The test performances of the Conic Section Function Neural Network with the proposed training versus Conic Section Function Neural Network with classical training are done on Iris, Lenses, Wine, Ecoli and Haberman datasets of UCI machine learning repository and Two Spiral Problem dataset.
Yazar
Ceyhun Çelik
Bu Yayına Nasıl Atıf Yapılır
Ceyhun Çelik (Master Thesis). Initialization and training improvements for conic section function neural networks, 2011, Çukurova University.
Anahtar Kelimeler
Lisans
Tüm Hakları Saklıdır
Bu eser belirtilen lisans koşulları altında paylaşılmaktadır.
Çukurova University tezlerinden daha fazlası
- An investigation of violent and nonviolent adolescent' families in terms in terms of family fuctioning, anger and anger expression(2006)
- Adolescents who have single parents family and full family were compared in respect to their life satisfaction and quality of life(2009)
- 9mm. identifying cartridge cases and bullets obtained from firearms and detection of brands(2020)
- Assessing morphological and genetic diversity among traditional African eggplant landraces and detecting salt tolerance and anther culture performance of selected accessions(2022)
- The effects of collaborative video-blog projects on Turkish EFL students' linguistic and digital literacy skills(2025)
- Cultural significance in foreign learning and teaching with special emphasis on reading comprehension through cultural schemata(1992)