整合學習在各大比賽中非常流程，如XGboost、LGBM等，對其基學習器決策樹及其剪枝等，可參考：

https://blog.csdn.net/fjssharpsword/article/details/54861274

整合學習可參考：

https://blog.csdn.net/fjssharpsword/article/details/61913092

關於Adaboost的matlab原始碼和圖示：

>> n=50;x=randn(n,2);y=2*(x(:,1)>x(:,2))-1;b=5000;
>> a=50;Y=zeros(a,a);yy=zeros(size(y));w=ones(n,1)/n;
>> X0=linspace(-3,3,a); [X(:,:,1) X(:,:,2)] = meshgrid(X0);
>> for j=1:b
      wy=w.*y;d=ceil(2*rand);[xs,xi]=sort(x(:,d));
      el=cumsum(wy(xi));eu=cumsum(wy(xi(end:-1:1)));
      e=eu(end-1:-1:1)-el(1:end-1);
      [em,ei]=max(abs(e));c=mean(xs(ei:ei+1));s=sign(e(ei));
      yh=sign(s*(x(:,d)-c));R=w'*(1-yh.*y)/2;
      t=log((1-R)/R)/2;yy=yy+yh*t;w=exp(-yy.*y);w=w/sum(w);
      Y=Y+sign(s*(X(:,:,d)-c))*t;
   end
>> figure(1);clf;hold on;axis([-3 3 -3 3]);
>> colormap([1 0.7 1;0.7 1 1]);contourf(X0,X0,sign(Y));
>> plot(x(y==1,1),x(y==1,2),'bo');
>> plot(x(y==-1,1),x(y==-1,2),'rx');
 

關於Adaboost通過指數損失來推導：