WLSVM is a custom Weka classifier designed to give Weka users an easy access 
to the effecient and fast SVM tool, Libsvm. WLSVM runs much faster 
than Weka SMO class and suports all Libsvm features like nu-SVM classification, 
one-class-SVM, and epsilon-SVM regression, and nu-SVM regression. 
As a Weka custom classifier, WLSVM allows Libsvm users to experiment with ARFF 
files and generate many useful statistics (e.g. Confusion matrix for cross validation 
tests, Percision, Recall, ROC scores).


WLSVM is available at:
http://www.cs.iastate.edu/~yasser/WLSVM


- How to use
	- unzip the file wlsvm.zip
	- Build a java project
	- Add the jars libsvm.jar and wlsvm.jar to your project
	- use WLSVM as any other weka classifier 

- Notes: 
        - your project should also have the weka.jar file.
        - An example java file is also distributed



WLSVM options :

-S <int>
	 set type of SVM (default 0)
		 0 = C-SVC
		 1 = nu-SVC
		 2 = one-class SVM
		 3 = epsilon-SVR
		 4 = nu-SVR
-K <int>
	 set type of kernel function (default 2)
		 0 = linear: u'*v
		 1 = polynomial: (gamma*u'*v + coef0)^degree
		 2 = radial basis function: exp(-gamma*|u-v|^2)
		 3 = sigmoid: tanh(gamma*u'*v + coef0)
-D <int>
	 set degree in kernel function (default 3)
-G <double>
	 set gamma in kernel function (default 1/k)
-R <double>
	 set coef0 in kernel function (default 0)
-C <double>
	 set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1)
-N <double>
	 set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)
-Z
	 whether to normalize input data, 0 or 1 (default 0)
-P <double>
	 set the epsilon in loss function of epsilon-SVR (default 0.1)
-M <double>
	 set cache memory size in MB (default 40)
-E <double>
	 set tolerance of termination criterion (default 0.001)
-H <int>
	 whether to use the shrinking heuristics, 0 or 1 (default 1)
-B <int>
	 whether to train a SVC or SVR model for probability estimates, 0 or 1 (default 0)
-W <double>
	 set the parameters C of class i to weight[i]*C, for C-SVC (default 1)

Citation 

If you would like to cite WLSVM, cite the following URL: 
Yasser EL-Manzalawy and Vasant Honavar, WLSVM : Integrating LibSVM into Weka Environment, 2005. Software available at http://www.cs.iastate.edu/~yasser/wlsvm 

The bibtex format is as follows 
@Manual{yh05,
  author =	 {Yasser EL-Manzalawy and Vasant Honavar},
  title =	 {{WLSVM}: Integrating LibSVM into Weka Environment},
  year =	 {2005},
  note =	 {Software available at \url{http://www.cs.iastate.edu/~yasser/wlsvm}}
}

Contacts 

If you have any problems using WLSVM or if you have any suggestions. Please contact Yasser EL-Manzalawy <yasser@cs.iastate.edu>

