LibSVM (original) (raw)

A wrapper class for the libsvm tools (the libsvm classes, typically the jar file, need to be in the classpath to use this classifier).
LibSVM runs faster than SMO since it uses LibSVM to build the SVM classifier.
LibSVM allows users to experiment with One-class SVM, Regressing SVM, and nu-SVM supported by LibSVM tool. LibSVM reports many useful statistics about LibSVM classifier (e.g., confusion matrix,precision, recall, ROC score, etc.).

Yasser EL-Manzalawy (2005). WLSVM. URL http://www.cs.iastate.edu/\~yasser/wlsvm/.

Chih-Chung Chang, Chih-Jen Lin (2001). LIBSVM - A Library for Support Vector Machines. URL http://www.csie.ntu.edu.tw/\~cjlin/libsvm/.

BibTeX:

@misc{EL-Manzalawy2005, author = {Yasser EL-Manzalawy}, note = {You don't need to include the WLSVM package in the CLASSPATH}, title = {WLSVM}, year = {2005}, URL = {http://www.cs.iastate.edu/\~yasser/wlsvm/} }

@misc{Chang2001, author = {Chih-Chung Chang and Chih-Jen Lin}, note = {The Weka classifier works with version 2.82 of LIBSVM}, title = {LIBSVM - A Library for Support Vector Machines}, year = {2001}, URL = {http://www.csie.ntu.edu.tw/\~cjlin/libsvm/} }

Valid options are:

-S Set type of SVM (default: 0) 0 = C-SVC 1 = nu-SVC 2 = one-class SVM 3 = epsilon-SVR 4 = nu-SVR

-K Set type of kernel function (default: 2) 0 = linear: u'v 1 = polynomial: (gammau'v + coef0)^degree 2 = radial basis function: exp(-gamma|u-v|^2) 3 = sigmoid: tanh(gamma*u'*v + coef0)

-D Set degree in kernel function (default: 3)

-G Set gamma in kernel function (default: 1/k)

-R Set coef0 in kernel function (default: 0)

-C Set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default: 1)

-N Set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default: 0.5)

-Z Turns on normalization of input data (default: off)

-J Turn off nominal to binary conversion. WARNING: use only if your data is all numeric!

-V Turn off missing value replacement. WARNING: use only if your data has no missing values.

-P Set the epsilon in loss function of epsilon-SVR (default: 0.1)

-M Set cache memory size in MB (default: 40)

-E Set tolerance of termination criterion (default: 0.001)

-H Turns the shrinking heuristics off (default: on)

-W Set the parameters C of class i to weight[i]*C, for C-SVC E.g., for a 3-class problem, you could use "1 1 1" for equally weighted classes. (default: 1 for all classes)

-B Generate probability estimates for classification

-seed Random seed (default = 1)