# 368809689 - 1 2 g ( x ) = w x + wo T x1 x 2 x= xd w1 w 2 w=...

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Unformatted text preview: 1 2 g ( x ) = w x + wo T x1 x 2 x= xd w1 w 2 w= wd 3 g ( x) = g1 ( x) - g 2 ( x) g ( x) > 0, x 1 g ( x) < 0, x 2 g ( x) = 0, x . g ( x) = 0 4 x1 , x2H w x1 + w0 = w x 2 + w0 T T w ( x1 - x 2 ) = 0 T 5 w w x = xp + r w 6 w ) + w0 g ( x) = w ( x p + r w T w w = w x p + w0 + r =r w w T T g ( x) r= w 7 : figure 4.2 g ( x ) = ( x - a )( x - b) g ( x ) = c0 + c1 x + c2 x = a y 2 T y1 1 y = x y = 2 2 y3 x a1 c0 a = c a = 2 1 a 3 c 2 8 9 g ( x) = w0 + w x = a y T T 1 w0 y = ,a = x w augmented sample vector 10 The Curse of Dimensionality 11 12 13 Dr=1 r=1 14 15 r 16 17 Fisher d N : x1 , , xN 1 : N1, 2 : N 2 N1 + N 2 = N 18 19 y n = w x n , n = 1,2, T , N i , i = 1,2 i = 1,2 1 mi = Ni x x, i 20 Si = x ( x - m )( x - m ) i i i T , i = 1,2 S i : S w = S1 + S 2 : S b = (m1 - m2 )(m1 - m2 ) T 21 ~ = 1 mi Ni yYi y, yYi i = 1,2 i = 1,2 ~2 ~ )2 , Si = ( y - m i ~ ~2 ~2 S w = S1 + S 2 ~ - m )2 ~ (m1 2 J F ( w) = ~ 2 ~ 2 S1 + S 2 22 ~ = 1 mi Ni 1 =w ( Ni T 1 y = N yYi i T x x w i T x x) = w m i i ~ - m ) 2 = ( wT m - wT m ) 2 ~ (m1 2 1 2 = w (m1 - m2 )(m1 - m2 ) w = w S b w T T T 23 ~2 Si = ~ )2 = ( y - mi yYi x (w i T x - w mi ) T 2 = w [ ( x - m i )( x - m i ) ]w T T x i = w Si w T 24 w Sb w J F ( w) = T w Sww T w S w w = c 0 T L = w S b w - ( w S w w - c) T T 25 L = S b w - S w w = 0 w Sb w = S w w S S b w = w -1 w 26 S b w = (m1 - m2 )(m1 - m2 ) w T = (m1 - m2 ) R w = S ( Sb w ) = S (m1 - m2 ) R -1 w -1 w w = S ( m1 - m2 ) 27 -1 w : y (1) 0 ~ ~ m1 + m2 = 2 ~ ~ N 2 m1 + N1m2 = N1 + N 2 ( y02 ) ( y03) ~ ~ m1 + m2 ln( P( w1 ) / P( w2 )) = + 2 N1 + N 2 - 2 y > y0 x w1 y < y0 x w2 28 : ,Bayes. 1. , . 2. 29 1. 2. 2. 3. 4. 5. Fisher? ? ? ? . ? ? ? ? 30 Fisher- 31 Fisher- : Fisher, ( ,) 32 Fisher ... 33 Fisher Fisher: Kernel Fisher Fisher Hastie T and Tibshirani R. Discriminant adaptive nearest neighbor classification. IEEE Trans. On PAMI, 1996, 18(6):409-415 NIPS ICML 34 a y T a T y i > 0, y i 1 T a y i < 0, y i 2 y i , y i 1 y = - y i , y i 2 ' n a y >0 T ' n 35 36 a , a y n > 0, n = 1,2, T , N. 37 J P (a) = k yY k (-a T y) Y J P (a) J P (a) = = a yY k (- y ) a (k + 1) = a (k ) - k J a(k + 1) = a(k ) + k yY y k 38 Algorithm Step 1: initialize Step 2: calculate a (0), k , t = 0 J P (a) J P (a) = = a yY k (- y ) yY k Step 3: Update a (k + 1) = a (k ) + k Step 4: if vector does not change, stop. else goto Step 2 y 39 Single Sample Correction Algorithm y1 , y2 ,..., yn , y1 , y2 ,..., yn ,...... a(k + 1) = a(k ) + y T k T k a (k + 1) y = a (k ) y + y y k kT k 40 c-1 c(c - 1) 2 41 42 43 44 45 46 47 ...
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## This note was uploaded on 06/02/2010 for the course ELECTRONIC PC2010S taught by Professor Zhangchangshui during the Spring '10 term at Tsinghua University.

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