Nurhan Türker

ROC analysis as a useful tool for performance evaluation of artificial neural networks

In many applications of neural networks, the performance of the network is given by the classification accuracy. While obtaining the classification accuracies, the total true classification is computed, but the number of classification rates of the classes and fault classification rates are not given. This would not be enough for a problem having fatal importance. As an implementation example, a dataset having fatal importance is classified by MLP, RBF, GRNN, PNN and LVQ networks and the real performances of these networks are found by applying ROC analysis.

A competitive approach to neural device modeling: support vector machines

Support Vector Machines (SVM) are a system for efficiently training linear learning machines in the kernel induced feature spaces, while respecting the insights provided by the generalization theory and exploiting the optimization theory. In this work, Support Vector Machines are employed for the nonlinear regression. The nonlinear regression ability of the Support Vector Machines has been demonstrated by forming the SVM model of a microwave transistor and it has been compared with its neural model.

Signal-noise support vector model of a microwave transistor: Research Articles

This article presents a new technique that uses the auxiliary sources for investigation of structures including nonlinear components. The proposed technique is implemented in the iterative method to model two transistors (MESFET and INGFET) and an MMIC amplifier. The numerical results are compared with published data and a good agreement is observed.

Determination of The Neural Network Performances In The Medical Prognosis By Roc Analysis

Recently, artificial neural networks are widely used in medical prognosis. The goal of this work is to predict whether a patient will live at least one year after a heart attack by using neural networks as an example of prognosis. With this aim, multi layer perceptrons (MLP), radial basis function networks (RBF), probabilistic neural networks (PNN), generalized regression neural networks (GRNN) and learning vector quantization networks (LVQ) are used. To demonstrate the real performances of the networks, not only classification accuracies but also receiver operation characteristics (ROC) analysis must be investigated. For this purpose, both sensitivity-specificity values and ROC curves are evaluated for all networks