Application of Support Vector Machine Model Based on Machine Learning in Art Teaching

The purpose of the evaluation is to reflect on whether education provides a good environment and conditions for development students effect teaching practicability talents cultivated by society. When art evaluated, number positive outcomes have been achieved in terms education, including improvement as whole, talent, stronger role educational social communities concerned about quality education. Machine learning-based support vector machine (SVM) can better tackle issues like nonlinearity, high dimensionality, local minima, which effectively implemented area (TQE) with fast growth information technology Internet. main work this paper follows: (1) it briefly expounds research progress TQE multiclassification algorithm based SVM at home abroad introduces relevant basic theories these two aspects. (2) One-to-one combination method used research, reducing training time certain degree. Tests prove procedure be objective equitable. (3) This claims that an approach suited limited professional assessment sample data purpose.