First let us learn what is a weak classifier h( -Case 1 If a point(x=lu, v Dis in the "gray area, h(x)=+I For this weak classifier p, m, c otherwise h(x=u, vD=-1. It can be written as are constants defining the classifier, variables h(r)=+I if v-mu<c, where m,x, are given constants 1 otherwise u, v are inputs. Vmu=fc Cas If u v is here,vw-mu=c If a point(x=[u, v )is in the"white"area, h(x)=+1-h(x=fu, v1=-1 otherwise h(x=u,v]=-1. It can be written as h(rs +1 if(v-mu<-cwhere-uy are given constants Gradient m otherwise V-mu>C At timet, combine case l and 2 together to become equation(() V-mu<c and use polarity p, to control which case you want to use if p,f(x)<p, B h,( If u, v is here, I otherwise h(X=[u,v])=1 where p, polarity .m c are used to define the line f is the function: (f(xu,vD=v-mu)and,=c .Any points in the gray area satisfy where m c are constants. u, v are variables V-mu<c plv-mu]<p,c, equation(()becomes .Any points in the white area h,(x)= Pv-m小<pc satisfy v-mu>c 1 otherwise Adaboost, vgaFirst let us learn what is a weak classifier h( ) •     ( ) 1 otherwise 1 if ( ) , ( ) becomes where are constants, are variables. is the function :( ( [ , ]) ,) and where polarity { 1or -1}, ( ) 1 otherwise 1 if ( ) ( ) and use polarity to control which case you want to use. --At time , combine case1and 2 together to become equation 1 otherwise 1 where , are given constants ( ) otherwise [ ] 1.It can be written as: If a point ( [ , ])isin the "white" area , 1 --Case2--1 otherwise 1 if ,where , are given constants ( ) otherwise [ ] 1.It can be written as: If a point ( [ ])isin the "gray"area , 1 --Case1-- ii p v mu p c h x p v mu p c equation i m,c u,v f f x u v v mu c p i p f x p h x p t (i) if -(v-mu) c , m,x h x h(x u,v ) x u v h(x) v-mu c m,x h x h(x u,v ) x u,v h(x) t t t t t t t t t t t t t − − − − − − − − −   − + −  = −  = − = = + − − − − − − − − −   − +  =   − +  − = = = − = = +   − +  = = = − = = +   9 Adaboost , V9a v=mu+c or v-mu=c •m,c are used to define the line •Any points in the gray area satisfy v-mu<c •Any points in the white area satisfy v-mu>c v c Gradient m (0,0) v-mu<c v-mu>c u For this weak classifier pt ,m,c are constants defining the classifier. Variables u,v are inputs. If u,v is here , h(x=[u,v])=1 If u,v is here , h(x=[u,v])=-1