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weka.SMOreg_PolyKernel

weka.SMOreg_PolyKernel

Visibility: public Uploaded 16-04-2017 by Thompson Weka_3.8.1 0 runs
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S.K. Shevade, S.S. Keerthi, C. Bhattacharyya, K.R.K. Murthy: Improvements to the SMO Algorithm for SVM Regression. In: IEEE Transactions on Neural Networks, 1999. A.J. Smola, B. Schoelkopf (1998). A tutorial on support vector regression.

Components

Kweka.PolyKernel(11)The Kernel to use. (default: weka.classifiers.functions.supportVector.PolyKernel)

Parameters

-do-not-check-capabilitiesIf set, classifier capabilities are not checked before classifier is built (use with caution).
CThe complexity constant C. (default 1)default: 1.0
EThe Exponent to use. (default: 1.0)
IOptimizer class used for solving quadratic optimization problem (default weka.classifiers.functions.supportVector.RegSMOImproved)default: weka.classifiers.functions.supportVector.RegSMOImproved -T 0.001 -V -P 1.0E-12 -L 0.001 -W 1
KThe Kernel to use. (default: weka.classifiers.functions.supportVector.PolyKernel)default: weka.classifiers.functions.supportVector.PolyKernel
LThe epsilon parameter in epsilon-insensitive loss function. (default 1.0e-3)
NWhether to 0=normalize/1=standardize/2=neither. (default 0=normalize)default: 0
PThe epsilon for round-off error. (default 1.0e-12)
TThe tolerance parameter for checking the stopping criterion. (default 0.001)
VUse variant 1 of the algorithm when true, otherwise use variant 2. (default true)
WThe random number seed. (default 1)
batch-sizeThe desired batch size for batch prediction (default 100).
no-checksTurns off all checks - use with caution! (default: checks on)
num-decimal-placesThe number of decimal places for the output of numbers in the model (default 2).
output-debug-infoIf set, classifier is run in debug mode and may output additional info to the console

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