consideration is non-stationary (i.e. has a unit root) or stationary. In the context of a Stochastic Volatility Model (SVM), the … propose a unit root test for the volatility process based on the Simulation-Extrapolation (SIMEX) approach. We express the SVM …

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 …

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 …

vectors. The proposed SVM training is implemented and tested on two problems: (i) gender classification of facial images using … standard SVM training, the proposed approach leads to much faster SVM training, produces a more compact classifier while …

credit risk assessment models based on support vector machine (SVM) technique and BP neural network respectly. Results: (1 …) The SCF credit risk assessment model based on SVM is of good generalization ability and robustness, which is more … applying the SVM model, which can alleviate credit rationing on SMEs. Conclusions: (1)The SCF credit risk assessment index …