中文说明：在葡萄酒制造业中,对于葡萄酒的分类具有很大意义,因为这涉及到不同种类的葡萄酒的存放以及出售价格,采用SVM做为分类器可以有效预测相关葡萄酒的种类,从UCI数据库中得到wine数据记录的是在意大利某一地区同一区域上三种不同品种的葡萄酒的化学成分分析,数据里含有178个样本分别属于三个类别(类别标签已给),每个样本含有13个特征分量(化学成分),将这178个样本50%做为训练样本,另50%做为测试样本,用训练样本对SVM分类器进行训练，用得到的模型对测试样本的进行分类标签预测，最终得到96.6292%的分类准确率.
English Description：
In wine manufacturing in the, for wine of classification has is big meaning, because this involves different species of wine of store and sold price, used SVM do for classification device can effective forecast related wine of species, from UCI database in the get wine data records of is in Italy a a area same regional Shang three species different varieties of wine of chemical components analysis, data in contains 178 a sample respectively belongs to three a category (category label has to), each sample contains 13 a features component ( Chemical composition), 50% 178 samples as training samples, another 50% sample for tests, SVM classifier is trained using training samples, use the model for prediction of classification and labelling of test samples, the resulting classification accuracy of 96.6292% .