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 + + Waxa + W2 + 1 ( 2.2 ) This function is a linear function of the components of X ; we shall denote ...
2 • 2 Linear discriminant functions Let us consider first the family of discriminant functions of the form g ( X ) = W1X1 + W2X2 + + Waxa + W2 + 1 ( 2.2 ) This function is a linear function of the components of X ; we shall denote ...
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 a respect to point sets .
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 a respect to point sets .
Sivu 32
... 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 = 2 ...
... 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 = 2 ...
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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 Development 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 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 regions respect response rule sample mean selection separable shown side solution space 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 |