In this paper the accuracy of two machine learning algorithms including SVM and Bayesian Network are investigated as two important algorithms in diagnosis of Parkinson’s disease. We use Parkinson's disease data in the University of California, Irvine (UCI). In order to optimize the SVM algorithm, different kernel functions and C parameters have been used and our results show that SVM with C par...

Kernel-based methods, such as support vector regression (SVR), have demonstrated satisfactory performance in short-term load forecasting (STLF) application. However, the good performance of kernel-based method depends on the selection of an appropriate kernel function that fits the learning target, unsuitable kernel function or hyper-parameters setting may lead to significantly poor performance...

The kernel-based classifiers use one of the classical kernels, but the real-world applications have emphasized the need to consider a new kernel function in order to boost the classification accuracy by a better adaptation of the kernel function to the characteristics of the data. Our purpose is to automatically design a complex kernel by evolutionary means. In order to achieve this purpose we ...

small area estimation is a technique used to estimate parameters of subpopulations with small sample sizes.  small area estimation is needed  in obtaining information on a small area, such as sub-district or village.  generally, in some cases, small area estimation uses parametric modeling.  but in fact, a lot of models have no linear relationship between the small area average and the covariat...