www.gusucode.com > stats 源码程序 matlab案例代码 > stats/MonitorTrainingOfSVMClassifierExample.m
%% Monitor Training of an SVM Classifier %% % Load the |ionosphere| data set. % Copyright 2015 The MathWorks, Inc. load ionosphere %% % Train an SVM classifier. For illustration, specify that the optimization % routine uses at most 100 iterations. Monitor the algorithm specifying % that the software prints diagnostic inofrmation every |50| iterations. SVMModel = fitcsvm(X,Y,'IterationLimit',100,'Verbose',1,'NumPrint',50); %% % The software prints an iterative display to the Command Window. The % printout indicates that the optimization routine has not converged onto a % solution. %% % Estimate the resubstitution loss of the partially trained SVM classifier. partialLoss = resubLoss(SVMModel) %% % The training sample misclassification error is approximately 11%. %% % Resume training the classifier for another |1500| iterations. Specify % that the software print diagnostic information every |250| iterations. UpdatedSVMModel = resume(SVMModel,1500,'NumPrint',250) %% % The software resumes at iteration |1000|, and uses the same verbosity % level as you set when you trained the model using |fitcsvm|. The % printout indicates that the algorithm converged. Therefore, % |UpdatedSVMModel| is a fully trained |ClassificationSVM| classifier. updatedLoss = resubLoss(UpdatedSVMModel) %% % The trainig sample misclassification error of the fully trained % classifier is approximately 8%.