Yasar, AliSaritas, Ismail2023-01-292023-01-292021Yasar, A., Saritas, I., (2021). Evaluation of The Classification Performance of Methods Developed in ANN Training. Selcuk University Journal of Engineering Sciences, 20 (01), 1-6.2757-8828https://hdl.handle.net/20.500.12395/45055In this study, the performance and success of some models used in the training of artificial neural networks are compared. The UCI machine learning database was used for the performance evaluation and comparison process and for the IRIS dataset), which we often encounter in performance evaluations. In the study, the classification performances of our data sets were calculated using Feedforward Neural Network Cross-Validation (FNN-CV), Probabilistic Neural Network Cross-Validation (PNN-CV) and Recurrent Neural Network Cross-Validation (RNNCV) learning techniques. Our data has been classified using a 10-fold cross validation technique. The RNN-CV method for the IRIS data set proved to be a good classification method by achieving 100% accuracy success which shows that it should be used in classification problems.eninfo:eu-repo/semantics/openAccessArtificial Neural NetworkCross-ValidationFeedforward Neural NetworkProbabilistic Neural NetworkRecurrent Neural NetworkArticle20116