--- a
+++ b/Ensemble Learning/AdaBoost/buildAdaBoost.m
@@ -0,0 +1,32 @@
+function abClassifier = buildAdaBoost(trnX, trnY, iter, tstX, tstY)
+if nargin < 4
+    tstX = [];
+    tstY = [];
+end
+abClassifier = initAdaBoost(iter);
+
+N = size(trnX, 1); % Number of training samples
+sampleWeight = repmat(1/N, N, 1);
+
+for i = 1:iter
+    weakClassifier = buildStump(trnX, trnY, sampleWeight);
+    abClassifier.WeakClas{i} = weakClassifier;
+    abClassifier.nWC = i;
+    % Compute the weight of this classifier
+    abClassifier.Weight(i) = 0.5*log((1-weakClassifier.error)/weakClassifier.error);
+    % Update sample weight
+    label = predStump(trnX, weakClassifier);
+    tmpSampleWeight = -1*abClassifier.Weight(i)*(trnY.*label); % N x 1
+    tmpSampleWeight = sampleWeight.*exp(tmpSampleWeight); % N x 1
+    sampleWeight = tmpSampleWeight./sum(tmpSampleWeight); % Normalized
+    
+    % Predict on training data
+    [ttt, abClassifier.trnErr(i)] = predAdaBoost(abClassifier, trnX, trnY);
+    % Predict on test data
+    if ~isempty(tstY)
+        abClassifier.hasTestData = true;
+        [ttt, abClassifier.tstErr(i)] = predAdaBoost(abClassifier, tstX, tstY);
+    end
+    % fprintf('\tIteration %d, Training error %f\n', i, abClassifier.trnErr(i));
+end
+end