Forecasting the Competitiveness of Technological Innovation Talents Using Least Squares Support Vector Machine

摘要: In this paper, we focus on the problem of forecasting competitiveness technological innovation talents, which is very important for modern enterprise management. To forecast an index system with hierarchical structure described, and four main categories are included, that is, 1) Technological talent stock competitiveness, 2) talents development environment 3) Technology utilization efficiency 4) sustainable competitiveness. solve disadvantages SVM, LSSVM used to map nonlinear input data a high dimensional feature space. Next, classifier talents. Finally, experimental results demonstrate effectiveness proposed algorithm.