Adaboost

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matlab实现了一下《统计机器学习》P140页的例子。adaboost是一个nonUniform的集成算法，提升错误样本的权重，降低正确样本的权重，再用分类器分类训练。

findbest函数，找到最优的decision stump的位置

%% findBest(X,Y,W) return best decision stump% parameter contains all the information of the basic classification% parameter:% ----index--------context-----%       1        correctRate%       2      decision stump%       3         direction% ----------------function [parameter,bestResult]=findBest(X,Y,W)decisionStump=(X+X+1)/2;bestCorrectRate=0;bestDecisionStump=0;bestSignal=0;for i=1:size(decisionStump,2)    A=zeros(1,10);    A(find(X<decisionStump(1,i)))=1;    A(find(X>=decisionStump(1,i)))=-1;    correctRate=sum((A==Y).*W);%correct rate    signal=1;    result=double(A==Y);    if correctRate<0.5        signal=-1;        correctRate=1-correctRate;        result=1-double(A==Y);    end    if correctRate>bestCorrectRate        bestCorrectRate=correctRate;        bestDecisionStump=decisionStump(1,i);        bestSignal=signal;        result(find(result==0))=-1;        bestResult=result;    endendparameter=[bestCorrectRate;bestDecisionStump;bestSignal];

主函数

%% adaboost function %% parameter contains all the information of the basic classification% parameter:% ----index--------context-----%       1        correctRate%       2      decision stump%       3         direction% -----------------------------% original dataX=[0,1,2,3,4,5,6,7,8,9];Y=[1,1,1,-1,-1,-1,1,1,1,-1];%init weightW=0.1*ones(1,10);% train model with less error by disition stumpfor i=1:4 % max iterator times    [parameter,bestResult]=findBest(X,Y,W);    errorRate=1-parameter(1,1);    amount=1/2*log((1-errorRate)/(errorRate));    % updata W    W=W.*exp(-(bestResult.*amount));    W=W/sum(W,2);end

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