下面以UCI中IRIS为例介绍一下数据集: ucidata\iris中有三个文件:Indexiris.datairis.namesindex为文件夹目录,列出了本文件夹里的所有文件,如iris中index的内容如下:Index of iris18 Mar 1996 105 Index08 Mar 1993 4551 iris.data30 May 1989 2604 iris.namesiris.data为iris数据文件,内容如下:5.1,3.5,1.4,0.2,Iris-setosa4.9,3.0,1.4,0.2,Iris-setosa4.7,3.2,1.3,0.2,Iris-setosa……7.0,3.2,4.7,1.4,Iris-versicolor6.9,3.1,4.9,1.5,Iris-versicolor……6.3,3.3,6.0,2.5,Iris-virginica6.4,3.2,4.5,1.5,Iris-versicolor5.8,2.7,5.1,1.9,Iris-virginica7.1,3.0,5.9,2.1,Iris-virginica……如上,属性直接以逗号隔开,中间没有空格(5.1,3.5,1.4,0.2,),最后一列为本行属性对应的值,即决策属性Iris-setosa。iris.names介绍了irir数据的一些相关信息,如数据标题、数据来源、以前使用情况、最近信息、实例数目、实例的属性等,如下所示部分:……7. Attribute Information:1. sepal length in cm2. sepal width in cm3. petal length in cm4. petal width in cm5. class:-- Iris Setosa-- Iris Versicolour-- Iris Virginica……9. Class Distribution: 33.3% for each of 3 classes.本数据的使用实例请参考其他论文,或本站后面的内容。下面以wine数据为例导入matlab并利用前面提到的libsvm做测试>> uiimport('wine.data')导入数据,workspace处出现wine数组178*14将标签和数据属性提取,并保存到matlab平台下的数据>> wine_label = wine(:,1);>> wine_data = wine(:,2:end);>> save winedat.mat(下次使用的时候可以直接>> load winedat)svm训练模型得到wine模型>> modelw = svmtrain(wine_data,wine_label);.*optimization finished, #iter = 239nu = 0.892184obj = -61.125695, rho = 0.131965nSV = 130, nBSV = 53.*optimization finished, #iter = 193nu = 0.882853obj = -50.421538, rho = -0.166754nSV = 107, nBSV = 42.*optimization finished, #iter = 214nu = 0.800233obj = -53.411663, rho = -0.286931nSV = 119, nBSV = 44Total nSV = 178分类结果>> [plabelw, accuracyw] = svmpredict(wine_label,wine_data,modelw);Accuracy = 100% (178/178) (classification)