@inproceedings{7a91cb52dfdd4189a5a9fadd86f66568,
title = "Elyazisi Verileri {\"U}zerinde YSA ve DVM' nin Siniflandirma Ba{\c s}arimlarinin Kar{\c s}ila{\c s}tirilmasi",
abstract = "This study is about the selection of classifiers in handwritten character recogntition. The aim of the study is to determine the most appropriate classifier type for a given handwritten character feature vector. PCA based features were classified by both Multilayer Artificial Neural Networks (ANN) and Support Vector Machines (SVM), than the recognition results were compared. We select Error Backpropagation, Resilient Backpropagation and Scaled Conjugate Gradients as ANN training methods, besides selected SVM kernel types are lineer, RBF and polynomial. The experimental results shows us the SVM has beter train and test performance with respect to ANN.",
author = "Fatih Kahraman and Abd{\"u}lkerim {\c C}apar and Alper Ayvaci and Hakan Demirel and Muhittin G{\"o}kmen",
year = "2004",
language = "T{\"u}rk{\c c}e",
isbn = "0780383184",
series = "Proceedings of the IEEE 12th Signal Processing and Communications Applications Conference, SIU 2004",
pages = "615--618",
editor = "B. Gunsel",
booktitle = "Proceedings of the IEEE 12th Signal Processing and Communications Applications Conference, SIU 2004",
note = "Proceedings of the IEEE 12th Signal Processing and Communications Applications Conference, SIU 2004 ; Conference date: 28-04-2004 Through 30-04-2004",
}