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本帖最后由 xmubingo 于 2013-3-31 14:26 编辑
------2013.3.31----------
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.html
1.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#
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