【摘要】 在当今的大数据时代,机器学习越来越广泛地应用于生活中,为人们解决实际生活生产的需要。其中,支持向量机算法是机器学习中重要的算法之一,应用广泛且有效。文章主要介绍了支持向量机的原理和模型,还介绍了核函数在支持向量机中的应用和交叉验证法的理论。在实验部分,文章将支持向量机应用于葡萄酒数据集上,通过分析对比,得到了以下的结论:对于葡萄酒数据集,采用R语言对多种支持向量机模型进行比较,当支持向量机的类型为nu-classification,核函数为线性核函数时,识别的正确率最高,达到了98.86%,并且应用交叉验证法进行验证,降低了识别误差。更多还原

【Abstract】 Today is the era of big data and machine learning is widely used in life to solve the needs of real life production. Among them, the support vector machine(SVM) algorithm is one of the important algorithms in machine learning, and it is widely used and effective. The article mainly introduces the principle and model of support vector machine, and also introduces the application of kernel function in SVM. In addition, the principle of cross-validation is introduced. In the experiments, the paper applies the SVM to the wine dataset. Through analysis and comparison, the following conclusions are obtained: For the wine dataset, a variety of support vector machine models are used for comparison. When the type of SVM is nu-classification, and the kernel function is the linear kernel, the recognition rate is the highest, which is 98.86%. In addition, the cross-validation method is applied in this paper,which reduces the recognition error.更多还原