banks in order to measure their client's degree of risk, and for rms 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 …

presents the use of support vector machine (SVM) algorithm for fault diagnosis through discrete wavelet features extracted from … computed for different types of classifiers such as artificial neural network (ANN), support vector machine (SVM) and J48 …

We propose a Support Vector Machine (SVM) based structural model in order to forecast the collapse of banking …. We train an SVM model to classify banks as solvent and insolvent. The resulting model exhibits significant ability in …

this effort, we applied a Support Vector Machines (SVM) technique for classification. The results show that we can achieve …

multilayer neural network model and SVM methodology to predict if a particular applicant can be classified as solvent or bankrupt … Neural Network models outperform the SVM models in terms of global good classification rates and of reduction of Error type I …. In fact, the good classification rates are respectively 90.2% (NNM) and 70.13% (SVM) for the in-sample set and the error …

quantitative analysis, Wavelet Transform (WT), Genetic Algorithm (GA) and Support Vector Machines (SVM) was proposed. WT was … a detail signal to eliminate the stochastic volatility. SVM were built to model the approximation signal …) relationship between the historical speeds thus to select the input of SVM from them, and Granger causality test was applied to …