Modeling of Entrepreneurship Activity Crisis Management by Support Vector Machine

The main goal of the study was to analyze the total and male entrepreneurial activity. Since it is the highly nonlinear task in this study was applied soft computing approach. Intelligent soft computing scheme support vector regression (SVR) was implemented. The performance of the proposed estimator was confirmed with the simulation results. According to the results, a greater improvement in estimation accuracy can be achieved through the SVR compared to other soft computing methodologies. The new optimization methods benefit from the soft computing capabilities of global optimization and multi-objective optimization rather than choosing a starting point by trial and error. A systematic approach was carried to predict the entrepreneurial activity by the SVR methodology. The performance of the SVR approaches compared to the results from ANN and GP showed interesting improvements in the prediction system. SVR predictions with the polynomial kernel function are superior to other methodologies in terms of root-mean-square error and coefficient of error. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.