determined by the neural structure 2.MLP 22-9-2. The model of Support Vector Machine shows a relatively high accuracy, but it is … Vector Machine and artificial neural networks, and to evaluate the results of the methods used. The data (balance sheets and … to create a model for predicting potential bankruptcy of companies using suitable classification methods, namely Support …

, and in the second step, important features are fed into the support vector machine to create a forecasting model. The … the decision boundary of the support vector machine with a linear kernel. The results reveal, inter alia, that support … vector machine with linear kernel shows 92.86% forecasting accuracy, while a support vector machine with radial basis …

prediction performance of random walk without drift. More specifically, this paper applies the random forest, support vector … machine, and neural network models to four fundamental theories (uncovered interest rate parity, purchase power parity, the …

-nearest neighbor algorithm, naïve Bayes, kernel naïve Bayes, decision trees, and support vector machine to distinguish between eleven …, followed by kernel naïve Bayes (91.9%), radial basis function (Gaussian) support vector machine (89.3%), linear discriminant …

. Therefore, we proposed the use of support vector machine for early differential diagnosis of PCa (SVM-PCa-EDD). SVM was used to …

. Design/methodology/approach - It can be seen that support vector machine (SVM) is used with particle swarm optimization (PSO …