模式分类英文课件pr10part1_ding

1、tc21c1jparameters mixingjdensities componentjj),.,( where)(P. ),|x(P)|x(P 0 x if )(21-11x if )(21 )1(21)1(21)|x(P2121x12x2x11x1 222211)x(21exp2)(P)x(21exp2)(P)|x(P c1jjjj)(P),|x(p)|x(p n1kk)|p(x)|p(D with)|D(pmaxarg ),|x(pln),x|(Pliikkn1kiii n1kk)|x(plnl ( i )(c)1,.,(i 0),|(ln),|(1axpxPnkiikkii c1jj

2、jjkiiikkiiikkin1kkii)(P),|x(p)(P),|x(p),x|(P :where0),|x(pln),x|(P and),x|(Pn1)(Pi 1iiti2/1i2/dii)x()x(21)2(ln),|x(pln i (1) ) ,|() ,|(11nkkinkkkiixPxxPi )0(i n1kkin1kkkii)j(,x|(Px)j(,x|(P)1j( 222121)x(21exp232)x(21exp231),|x(p n1k21k21),|x(pln),( l668. 1 and 130. 221 257. 1 and 085. 221 222x21exp22

3、1x21exp.221),|x(p 212121exp221 221),|(xxp n2k2kn0 term this212n1x21exp)22(1x21exp1),|x,.,x(p 2k2kx21exp221),|x(p nkkinktikikkiinkkinkkkiinkkiixPxxxPxPxxPxPnP11111),|()()(,|(),|(),|(),|(1)(Iterative schemecjjjkjtjkjiikitikicjjijkiiikkiPxxPxxPxpPxpxP112/112/11)()()(21exp)()()(21exp)(),|()(),|(),|(2iki

4、k1itikxdistance Euclidean squaredthe by )x()x( m),x|(Pki otherwise 0mi if 1),x|(Pki c21,., ),x|(Pki nkkinkkkiixPxxP11),|(),|()(P)(jP)|(DP),|(jjxp)(PcjjjjPxpxp1)(),|()|(cjjjiicjjjiiiPDpPDpDPDpDPDpDP11)(),|()(),|()|(),|()|(),|(),|(xxxxxxxxxdDppd,Dp,D,pdDpDpiiiiii)|(),|( )()|(),|,(),|( We can compute p

5、( /D) as seen previouslyand the unknown parameter vector is introduced to represent the class conditonal density. Hence, the best estimate of p(x/i) is obtained by averaging p(x /i, i) over i. Since all depends from p( /D), this is the main issue of the problem.nkkpDp1)|( )|(xxxdDppDppDpnnnnn)|()|()|()|( )|(11dpDppDpDp)()|()()|()|(),|(),|(xxiipDpcjjjjiiiiPpPpDP1)(),|()(),|(),|(xxx)|()|()(),|()(),|()|()|()|()|( )|(111111ncjnjjjncjj