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    %% Resume Training an SVM Classifier
% If you trained an SVM classifier, and the solver failed to converge onto
% a solution, then you can resume training the classifier without having to
% restart the entire learning process.
%%
% Load the |ionosphere| data set.

% Copyright 2015 The MathWorks, Inc.

load ionosphere
rng(1); % For reproducibility
%%
% Train an SVM classifier. For illustration, specify that the optimization
% routine uses at most 50 iterations.
SVMModel = fitcsvm(X,Y,'IterationLimit',50);
DidConverge = SVMModel.ConvergenceInfo.Converged
Reason = SVMModel.ConvergenceInfo.ReasonForConvergence
%%
% |DidConverge = 0| indicates that the optimization routine did not
% converge onto a solution.  |Reason| states the reaon why the routine did
% not converge.  Therefore, |SVMModel| is a partially trained, SVM
% classifier.
%%
% Resume training the SVM classifier for another |1500| iterations.
UpdatedSVMModel = resume(SVMModel,1500);
DidConverge = UpdatedSVMModel.ConvergenceInfo.Converged
Reason = UpdatedSVMModel.ConvergenceInfo.ReasonForConvergence
%%
% |DidConverge| indicates that the optimization routine converged onto a
% solution. |Reason| indicates that the gradient difference
% (|DeltaGradient|) reached its tolerance level (|DelatGradientTolerance|).
% Therefore, |SVMModel| is a fully trained SVM classifier.