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weka.LWL_SMO_PolyKernel

weka.LWL_SMO_PolyKernel

Visibility: public Uploaded 31-07-2017 by Miguel Cachada Weka_3.8.1 0 runs
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Eibe Frank, Mark Hall, Bernhard Pfahringer: Locally Weighted Naive Bayes. In: 19th Conference in Uncertainty in Artificial Intelligence, 249-256, 2003. C. Atkeson, A. Moore, S. Schaal (1996). Locally weighted learning. AI Review..

Components

Wweka.SMO_PolyKernel(15)Full name of base classifier. (default: weka.classifiers.trees.DecisionStump)

Parameters

-do-not-check-capabilitiesIf set, classifier capabilities are not checked before classifier is built (use with caution).
AThe nearest neighbour search algorithm to use (default: weka.core.neighboursearch.LinearNNSearch).default: true
KSet the number of neighbours used to set the kernel bandwidth. (default all)default: -1
USet the weighting kernel shape to use. 0=Linear, 1=Epanechnikov, 2=Tricube, 3=Inverse, 4=Gaussian. (default 0 = Linear)default: 0
WFull name of base classifier. (default: weka.classifiers.trees.DecisionStump)default: weka.classifiers.functions.SMO
batch-sizeThe desired batch size for batch prediction (default 100).
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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