The Digital Intelligent Application of Experiment Teaching Based on SVM-Adaboost Algorithm

Abstract

This study applied support vector machine algorithm and adaptive-boost algorithm to analyze the best division hyperplane of enterprise resource planning experimental teaching. We used two groups of experimental data to apply support vector machine and adaptive-boost algorithm. To complete data preprocessing and assign different weights of each index we applied adaptive-boost algorithm. Then we used the SVM to calculate and classify the expected samples. After two sets of experiments, the results show that the expected samples classified by support vector machine and adaptive-boost algorithm have a better fit with the actual experimental situation. It means that the algorithm improves the ability of digital intelligent prediction and feedback in experimental teaching. It supplies a reference for the experimental teaching of the immersive economy and management major in the future.