Effect of templateSV​M('Standar​dize',true​);

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Mustafa Sami
Mustafa Sami 2020-3-30
评论： c 2020-10-2
Dear all,
Is there any documentation that explains the advantage of using
t = templateSVM('Standardize',true)
when using
classifier = fitcecoc(featuresTrain,YTrain,'Learners',t);
Because in my case it provides a better classification result, but need to understand some basics on how it works.
Any comment is appreciated.
Meshoo
  3 个评论
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c
c 2020-10-2
%% template = templateSVM('Standardize',true)
% 'BoxConstraint' — 1 (default)
% 'CacheSize' — 1000 (default)
% 'ClipAlphas' — true (default)
% 'DeltaGradientTolerance' — 0 if the solver is ISDA (for example, you set 'Solver','ISDA')
% 'GapTolerance' -0 (default)
% 'IterationLimit' — 1e6 (default)
% 'KernelFunction' — 'linear' Linear kernel, default for two-class learning
% 'KernelOffset' -0.1 if the solver is ISDA (that is, you set 'Solver','ISDA')
% 'KernelScale' — 1 (default)
% 'KKTTolerance' — Karush-Kuhn-Tucker complementarity conditions violation tolerance
% 1e-3 if the solver is ISDA (for example, you set 'Solver','ISDA')
% 'NumPrint' — 1000 (default)
% 'OutlierFraction' —0 (default)
% 'SaveSupportVectors' —true (default) Store support vectors, their labels, and the estimated α coefficients
% 'ShrinkagePeriod' — 0 (default)
% 'Solver' — The default value is 'ISDA'
% 'Standardize' — false (default)
% 'Verbose' — 0 (default)
c
c 2020-10-2
template = templateSVM('Standardize',true)-->
it means using all the default values.

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