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B

biasTipText() - Method in class weka.classifiers.functions.LibLINEAR
Returns the tip text for this property
buildClassifier(Instances) - Method in class weka.classifiers.functions.LibLINEAR
builds the classifier

C

costTipText() - Method in class weka.classifiers.functions.LibLINEAR
Returns the tip text for this property

D

distributionForInstance(Instance) - Method in class weka.classifiers.functions.LibLINEAR
Computes the distribution for a given instance.

E

epsilonParameterTipText() - Method in class weka.classifiers.functions.LibLINEAR
Returns the tip text for this property
epsTipText() - Method in class weka.classifiers.functions.LibLINEAR
Returns the tip text for this property

G

getBias() - Method in class weka.classifiers.functions.LibLINEAR
Returns bias term value (default 1) No bias term is added if value < 0
getCapabilities() - Method in class weka.classifiers.functions.LibLINEAR
Returns default capabilities of the classifier.
getCost() - Method in class weka.classifiers.functions.LibLINEAR
Returns the cost parameter C
getEps() - Method in class weka.classifiers.functions.LibLINEAR
Gets tolerance of termination criterion
getEpsilonParameter() - Method in class weka.classifiers.functions.LibLINEAR
Get the value of epsilon parameter of the epsilon insensitive loss function.
getMaximumNumberOfIterations() - Method in class weka.classifiers.functions.LibLINEAR
Get the number of iterations to perform.
getModel() - Method in class weka.classifiers.functions.LibLINEAR
 
getNormalize() - Method in class weka.classifiers.functions.LibLINEAR
whether to normalize input data
getOptions() - Method in class weka.classifiers.functions.LibLINEAR
Returns the current options
getProbabilityEstimates() - Method in class weka.classifiers.functions.LibLINEAR
Sets whether to generate probability estimates instead of -1/+1 for classification problems.
getRevision() - Method in class weka.classifiers.functions.LibLINEAR
Returns the revision string.
getSVMType() - Method in class weka.classifiers.functions.LibLINEAR
Gets type of SVM
getTechnicalInformation() - Method in class weka.classifiers.functions.LibLINEAR
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getWeights() - Method in class weka.classifiers.functions.LibLINEAR
Gets the parameters C of class i to weight[i]*C (default 1).
globalInfo() - Method in class weka.classifiers.functions.LibLINEAR
Returns a string describing classifier

L

LibLINEAR - Class in weka.classifiers.functions
A wrapper class for the liblinear classifier.
Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, Chih-Jen Lin (2008).
LibLINEAR() - Constructor for class weka.classifiers.functions.LibLINEAR
 
listOptions() - Method in class weka.classifiers.functions.LibLINEAR
Returns an enumeration describing the available options.

M

main(String[]) - Static method in class weka.classifiers.functions.LibLINEAR
Main method for testing this class.
maximumNumberOfIterationsTipText() - Method in class weka.classifiers.functions.LibLINEAR
Returns the tip text for this property

N

normalizeTipText() - Method in class weka.classifiers.functions.LibLINEAR
Returns the tip text for this property

P

probabilityEstimatesTipText() - Method in class weka.classifiers.functions.LibLINEAR
Returns the tip text for this property

R

REVISION - Static variable in class weka.classifiers.functions.LibLINEAR
 

S

setBias(double) - Method in class weka.classifiers.functions.LibLINEAR
Sets bias term value (default 1) No bias term is added if value < 0
setCost(double) - Method in class weka.classifiers.functions.LibLINEAR
Sets the cost parameter C (default 1)
setEps(double) - Method in class weka.classifiers.functions.LibLINEAR
Sets tolerance of termination criterion (default 0.001)
setEpsilonParameter(double) - Method in class weka.classifiers.functions.LibLINEAR
Set the value of epsilon parameter of the epsilon insensitive loss function.
setMaximumNumberOfIterations(int) - Method in class weka.classifiers.functions.LibLINEAR
Set the number of iterations to perform.
setNormalize(boolean) - Method in class weka.classifiers.functions.LibLINEAR
whether to normalize input data
setOptions(String[]) - Method in class weka.classifiers.functions.LibLINEAR
Sets the classifier options

Valid options are:

setProbabilityEstimates(boolean) - Method in class weka.classifiers.functions.LibLINEAR
Returns whether probability estimates are generated instead of -1/+1 for classification problems.
setSVMType(SelectedTag) - Method in class weka.classifiers.functions.LibLINEAR
Sets type of SVM (default SVMTYPE_L2)
setWeights(String) - Method in class weka.classifiers.functions.LibLINEAR
Sets the parameters C of class i to weight[i]*C (default 1).
SVMTypeTipText() - Method in class weka.classifiers.functions.LibLINEAR
Returns the tip text for this property

T

TAGS_SVMTYPE - Static variable in class weka.classifiers.functions.LibLINEAR
SVM solver types
toString() - Method in class weka.classifiers.functions.LibLINEAR
returns a string representation

W

weightsTipText() - Method in class weka.classifiers.functions.LibLINEAR
Returns the tip text for this property
weka.classifiers.functions - package weka.classifiers.functions
 
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