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weka.classifiers.functions.SMO(weka.classifiers.functions.supportVector.PolyKernel,weka.classifiers.functions.Logistic)_1452c951-cd46-46b6-bcfc-8ce4460f2838

weka.classifiers.functions.SMO(weka.classifiers.functions.supportVector.PolyKernel,weka.classifiers.functions.Logistic)_1452c951-cd46-46b6-bcfc-8ce4460f2838

Visibility: public Uploaded 18-10-2024 by Jan van Rijn Weka_3.9.6 0 runs
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J. Platt: Fast Training of Support Vector Machines using Sequential Minimal Optimization. In B. Schoelkopf and C. Burges and A. Smola, editors, Advances in Kernel Methods - Support Vector Learning, 1998. S.S. Keerthi, S.K. Shevade, C. Bhattacharyya, K.R.K. Murthy (2001). Improvements to Platt's SMO Algorithm for SVM Classifier Design. Neural Computation. 13(3):637-649. Trevor Hastie, Robert Tibshirani: Classification by Pairwise Coupling. In: Advances in Neural Information Processing Systems, 1998.

Parameters

-do-not-check-capabilitiesIf set, classifier capabilities are not checked before classifier is built (use with caution).default: ["false"]
CThe complexity constant C. (default 1)default: ["1.0"]
KThe Kernel to use. (default: weka.classifiers.functions.supportVector.PolyKernel)default: ["weka.classifiers.functions.supportVector.PolyKernel"]
LThe tolerance parameter. (default 1.0e-3)default: ["0.001"]
MFit calibration models to SVM outputs.default: ["false"]
NWhether to 0=normalize/1=standardize/2=neither. (default 0=normalize)default: ["0"]
PThe epsilon for round-off error. (default 1.0e-12)default: ["1.0E-12"]
VThe number of folds for the internal cross-validation. (default -1, use training data)default: ["-1"]
WThe random number seed. (default 1)default: ["1"]
batch-sizeThe desired batch size for batch prediction (default 100).default: []
calibratorFull name of calibration model, followed by options. (default: "weka.classifiers.functions.Logistic")default: ["weka.classifiers.functions.Logistic"]
no-checksTurns off all checks - use with caution! Turning them off assumes that data is purely numeric, doesn't contain any missing values, and has a nominal class. Turning them off also means that no header information will be stored if the machine is linear. Finally, it also assumes that no instance has a weight equal to 0. (default: checks on)default: ["false"]
num-decimal-placesThe number of decimal places for the output of numbers in the model (default 2).default: []
output-debug-infoIf set, classifier is run in debug mode and may output additional info to the consoledefault: ["false"]

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