Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 16
2 : 2 Linear discriminant functions Let us consider first the family of discriminant
functions of the form g ( x ) = W1X1 + W2X2 + . . . + WdXd + wa + 1 ( 2 . 2 ) This
function is a linear function of the components of X ; we shall denote discriminant
...
2 : 2 Linear discriminant functions Let us consider first the family of discriminant
functions of the form g ( x ) = W1X1 + W2X2 + . . . + WdXd + wa + 1 ( 2 . 2 ) This
function is a linear function of the components of X ; we shall denote discriminant
...
Sivu 24
2 : 7 Piecewise linear discriminant functions As a special case of discriminant
functions which we shall call piecewise linear , we shall first consider those of a
minimum - distance classifier with respect to point sets . Suppose we are given R
...
2 : 7 Piecewise linear discriminant functions As a special case of discriminant
functions which we shall call piecewise linear , we shall first consider those of a
minimum - distance classifier with respect to point sets . Suppose we are given R
...
Sivu 32
... number of ways in which N points can be partitioned by a ( d – 1 ) -
dimensional hyperplane . ( For each distinct partition , there are two different
classifications ) . Before obtaining a general expression for L ( N , d ) consider the
case N = 4 , d ...
... number of ways in which N points can be partitioned by a ( d – 1 ) -
dimensional hyperplane . ( For each distinct partition , there are two different
classifications ) . Before obtaining a general expression for L ( N , d ) consider the
case N = 4 , d ...
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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 Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed gi(X given implemented important initial layered machine linear dichotomies linear machine linearly separable matrix measurements networks 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 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 |