1、蠓蟲分類解：（1）Bayes判別：求條件概率假設假設兩類數據均服從二維正態分布， 則條件概率為：決策規則為; 或等價地: 判決函數：類似地，Bayes最小風險判別可通過給出風險后得到。x=1.201.301.181.141.261.281.361.481.401.38 1.24 1.381.541.381.561.241.281.401.221.36;1.861.961.781.782.002.001.741.821.701.90 1.72 1.641.821.822.081.801.782.041.881.78;n1=6;n2=9;n3=5;plot(x(1,1:n1),x(2,1:n1),

2、'o',x(1,n1+1:n1+n2),x(2,n1+1:n1+n2),'*',x(1,n1+n2+1:end),x(2,n1+n2+1:end),'r+');mm1=sum(y(1:n1)/n1;mm2=sum(y(n1+1:n1+n2)/n2;sgm1=cov(x(:,1:n1)'); %=s1/(n1-1);sgm2=cov(x(:,n1+1:n1+n2)');X=x(:,n1+n2+1:end);X=X'pxw1= mvnpdf(X, m1', sgm1);pxw2= mvnpdf(X, m2',

3、sgm2);pwx1=pxw1./(pxw1+pxw2);pwx2=pxw2./(pxw1+pxw2);display('Using Bayes principal is:')Apf=find(pwx1>pwx2)+n1+n2,（2）Fisher判別：求投影方向w*準則函數：其中最優解：m1=mean(x(:,1:n1),2);m2=mean(x(:,n1+1:n1+n2),2);s1=(x(:,1:n1)-repmat(m1,1,n1)*(x(:,1:n1)-repmat(m1,1,n1)'s2=(x(:,n1+1:n1+n2)-repmat(m2,1,n2)*

4、(x(:,n1+1:n1+n2)-repmat(m2,1,n2)'S=s1+s2;w=inv(S)*(m1-m2);y=w'*x;mm1=sum(y(1:n1)/n1;mm2=sum(y(n1+1:n1+n2)/n2;y0=(mm1+mm2)/2;%y0=(mm1*n1+mm2*n2)/(n1+n2);dpyb=y(n1+n2+1:end);display('Using fisher principal is:')Apf=find(dpyb>y0)+n1+n2,figure(2);t=1.1:0.01:1.6;kkk=-w(1)/w(2);ft=kkk*t

5、+y0/w(2);plot(x(1,1:n1),x(2,1:n1),'o',x(1,n1+1:n1+n2),x(2,n1+1:n1+n2),'*',x(1,n1+n2+1:end),x(2,n1+n2+1:end),'r+',t,ft);axis(1.1,1.6,1.6,2.1);感知器準則及梯度下降算法：樣本的增廣化： 樣本的規范增廣化：（仍記為）目標：求 使梯度下降法：批處理感知器算法固定增量單樣本感知器%peceptronx1=ones(1,length(x);x;x1(:,n1+1:n1+n2)=-x1(:,n1+1:n1+n2);eps

6、l=0.1;a=1-2*rand(3,1);k=0;while k<100000 k=k+1; y=x1(:,rem(k,n1+n2)+1); if a'*y<epsl a=a+y; end end y1=a'*x1(:,1:n1+n2);ind=find(y1<=0);display('the samples for first class using peceptron principal is:')Apf=find(a'*x1(:,n1+n2+1:end)>0)pt=-a(2)*t/a(3)-a(1)/a(3);figure

7、(3),plot(x(1,1:n1),x(2,1:n1),'o',x(1,n1+1:n1+n2),x(2,n1+1:n1+n2),'*',x(1,n1+n2+1:end),x(2,n1+n2+1:end),'r+',t,pt,ft);最小平方誤差準則： 設其中，目標： min 目標函數的梯度：令 %MSE b=ones(n1+n2,1);Y=x1(:,1:n1+n2);Y=Y'Yplus=(Y'*Y)(-1)*Y'ahat=Yplus*b;mt=-ahat(2)*t/ahat(3)-ahat(1)/ahat(3);figure(4)plot(t,pt;ft;mt,x(1,1:n1),x(2,1:n1),'o',x(1,n1+1:n1+n2),x(2,n1+1:n1+n2),'*',x(1,n1+n2+1:end),x(2,n1+n2+1:end),'r+