Learning Machines: Foundations of Trainable Pattern-classifying Systems |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 31
Sivu 29
The first d components of F are xı ? , x2 , . . . , Xa ? ; the next dd – 1 ) / 2
components are all the pairs X1X2 , X1X 3 , . . . , Xd – 1Xd ; the last d components
are x1 , x2 , . . . , Xd . The total number of 1 , 2 = f , g ( x ) Discriminant Summing
device ...
The first d components of F are xı ? , x2 , . . . , Xa ? ; the next dd – 1 ) / 2
components are all the pairs X1X2 , X1X 3 , . . . , Xd – 1Xd ; the last d components
are x1 , x2 , . . . , Xd . The total number of 1 , 2 = f , g ( x ) Discriminant Summing
device ...
Sivu 90
inaccurately classified as a member of Y , when it actually belongs to Yi ; Zx is
then expressed by Zx = Zir ( Ựk ) ( 5 : 33 ) That is , Z « is a vector whose ith block
of D components is set equal to Yk , whose Ith block of D components is set equal
...
inaccurately classified as a member of Y , when it actually belongs to Yi ; Zx is
then expressed by Zx = Zir ( Ựk ) ( 5 : 33 ) That is , Z « is a vector whose ith block
of D components is set equal to Yk , whose Ith block of D components is set equal
...
Sivu 111
Since U is a binary vector with P components there are 2P distinct U vectors . For
some of these U vectors , H ( U ) = 1 , and for the remaining , H ( U ) = – 1 . Let Hi
be a matrix whose rows consist of those U vectors for which H ( U ) = + 1 .
Since U is a binary vector with P components there are 2P distinct U vectors . For
some of these U vectors , H ( U ) = 1 , and for the remaining , H ( U ) = – 1 . Let Hi
be a matrix whose rows consist of those U vectors for which H ( U ) = + 1 .
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adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding covariance decision surfaces define denote density depends derivation described discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial layered machine linear dichotomies linear machine linearly separable matrix measurements negative normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem proof properties proved PWL machine quadric reduced regions respect response rule sample mean selection separable shown side solution space specific Stanford step Suppose theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |
Kirjaluettelon tiedot
| Otsikko | Learning Machines: Foundations of Trainable Pattern-classifying Systems McGraw-Hill series in systems science |
| Kirjoittaja | Nils J. Nilsson |
| Painos | kuvitettu |
| Kustantaja | McGraw-Hill, 1965 |
| ISBN | 0070465703, 9780070465701 |
| Pituus | 137 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |