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/.
(based on WEKA 3.6)
For further options, click the 'More' - button in the dialog.
All weka dialogs have a panel where you can specify classifier-specific parameters.
The Preliminary Attribute Check tests the underlying classifier against the DataTable specification at the inport of the node. Columns that are compatible with the classifier are marked with a green 'ok'. Columns which are potentially not compatible are assigned a red error message.
Important: If a column is marked as 'incompatible', it does not necessarily mean that the classifier cannot be executed! Sometimes, the error message 'Cannot handle String class' simply means that no nominal values are available (yet). This may change during execution of the predecessor nodes.
Capabilities: [Nominal attributes, Binary attributes, Unary attributes, Empty nominal attributes, Numeric attributes, Date attributes, Nominal class, Binary class, Missing class values] Dependencies: [Nominal class, Binary class, Unary class, Numeric class, Date class] min # Instance: 1
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: (gamma*u'*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: Trains a SVC model instead of a SVR one (default: SVR)
D: If set, classifier is run in debug mode and may output additional info to the console
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