adaboost的matlab实现代码，适合给初学者看

%% % File Name: runAdaBoosing % This is the "main" it runs the ada boost algorithm and for different sizes of % training sets. It provides a graph of the errors according to the size of the % training set. %% %!Important: run this commands in the Matlab "Command Window" before you run %open usps.mat; %load usps.mat; function runAdaBoosting(train,train_label,test,test_label) disp('run adaboost with cycles=100'); step=100; cycles=100; for m=step:step:1000 disp(m); boost=adaBoost(train(1:m,:),train_label(1:m),cycles); [errorTrain(m/step),errorTest(m/step)]=getError(boost,train(1:m,:),train_label(1:m),test,test_label); end % image figure No. 1 f1h = figure; % set various properties and display images set(f1h,'units','normalized'); set(f1h,'position',[0 0 0.5 1]); set(f1h,'numbertitle','off'); titleName='adaboost first work'; set(f1h,'name',titleName); X = step : step : 1000 ; plot(X,errorTrain,'r+:',X,errorTest,'bd-'); title('adaboost vs. size of train file','Fontsize',16); xlabel('num of samples (on train file)'); ylabel('Errrors rate'); legend('error on train file','error on test file'); grid on; clear errorTrain; clear errorTest; disp('run adaboost algorothm with different boosting'); step=20; maxCycles=100; for cycles=step:step:maxCycles disp(cycles); boost=adaBoost(train,train_label,cycles); [errorTrain(cycles/step),errorTest(cycles/step)]=getError(boost,train,train_label,test,test_label); clear boost; end % image figure No. 2 f2h = figure; % set various properties and display images set(f2h,'units','normalized'); set(f2h,'position',[0 0 0.5 1]); set(f2h,'numbertitle','off'); set(f2h,'name',titleName); X = step : step : maxCycles ; plot(X,errorTrain,'r+:',X,errorTest,'bd-'); title('adaboost vs. boosting cycles','Fontsize',16); xlabel('num of samples (on train file)'); ylabel('Errrors rate'); legend('error on train file','error on test file'); grid on;