weka下运行libsvm的方法
http://datamining.xmu.edu.cn/bbs/forum.php?mod=viewthread&tid=120
weka 3.6.9用底下的“旧方法”安装失败！
抛出“problem evaluating classifier rand”错误！
正确办法：
1. 先安装WEKA3.7.9，用tools-package manager安装libsvm，然后到用户目录下wekafiles\packages\LibSVM\lib中找到libsvm.jar 。
2. 安装WEKA3.6.9，将第一步中的libsvm.jar放到WEKA3.6.9的安装目录下。（）
3. 修改WEKA3.6.9的RunWeka.ini文件，将
- cmd_default=javaw -Dfile.encoding=#fileEncoding# -Xmx#maxheap# #javaOpts# -classpath "#wekajar#;#cp#" #mainclass#
复制代码
替换为：
- cmd_default=javaw -Dfile.encoding=#fileEncoding# -Xmx#maxheap# #javaOpts# -classpath "#wekajar#;#cp#;libsvm.jar" #mainclass#
复制代码
4. 运行WEKA3.6.9即可。WEKA3.7.9可以卸载了。
备注：
1. weka 3.7.9 直接用tools-package manager安装libsvm
2. 3.7.9和3.6.9存在许多差别。比如：3.7.9把rotationforest分类器搞没了。。
-----旧方法----
来自网络：http://blog.csdn.net/chl033/archive/2009/09/26/4597959.aspx
Weka and LibSVM are two efficient software tools for building SVM classifiers. Each one of these two tools has its points of strength and weakness. Weka has a GUI and produces many useful statistics (e.g. confusion matrix, precision, recall, F-measure, and
ROC scores). LibSVM runs much faster than Weka SMO and supports several SVM methods (e.g. One-class SVM, nu-SVM, and R-SVM). Weka LibSVM (WLSVM) combines the merits of the two tools. WLSVM can be viewed as an implementation of the LibSVM running under Weka
environment.
官方网站：http://www.cs.waikato.ac.nz/~ml/weka/index.html1.下载 wlsvm(weka libsvm) 地址：http://www.cs.iastate.edu/~yasser/wlsvm/
2.解压wlsvm.zip在lib目录下得到 libsvm.jar和wlsvm.jar两个文件，将其拷贝到weka安装目录下
3.修改位于weka安装目录下的RunWeka.ini文件
修改cmd_default=javaw -Dfile.encoding=#fileEncoding# -Xmx#maxheap# -classpath "#wekajar#;#cp#" #mainclass#
为cmd_default=javaw -Dfile.encoding=#fileEncoding# -Xmx#maxheap# -classpath "#wekajar#;#cp#;libsvm.jar" #mainclass#