banks in order to measure their client's degree of risk, and for firms to operate successfully. The SVM with evolutionary … and probit models as benchmark On overall, GA-SVM is outperforms compared to the benchmark models in both training and …

Support Vector Machine (SVM) and a logistic regression (Logit). Among different financial ratios suggested as predictors of … accuracy the SVM has a lower model risk than the Logit on average and displays a more robust performance. This result holds …

nonparametric approach based on a combination of kernel logistic regression and ¡support vector regression. …

nonparametric approach based on a combination of kernel logistic regression and ¡support vector regression. …

nonparametric approach based on a combination of kernel logistic regression and ¡support vector regression. …

-gram frequency vectors and train a support vector machine (SVM), relying only on titles, abstracts and IPC categorization of each … document. Altering the utilized Kernel functions and respective parameters we reach a recall level of 83% and precision level …

elections; 2016 and 2020 US. Three models, Support Vector Machine (SVM), Neural Network (NN) and ARIMA models are then used to … individual models, SVM, NN, and ARIMA. In addition, this compared to the single prediction market IOWA Electronic Markets. The …