The chemical composition of wine is the key basis for recognizing its varieties,but the conventional testing methods are complicated,cumbersome and with low accuracy.In the era of big data,machine learning is widely used in life,and the support vector machine algorithm,as one of the important algorithms of machine learning,is widely used and effective.Therefore,this study adopts the machine learning method to recognize wine types,and describes the principle and model of support vector machine,as well as the application of kernel function and the theory of cross-validation method.The results show that the model based on support vector machine has better classification performance with 98.87%accuracy,better global convergence,and can overcome the shortcomings of K-mean algorithm.
support vector machinewine qualityK-mean algorithm
维普
万方数据
