Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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The sign of g ( x ) can be evaluated by a threshold element whose threshold
value is equal to zero . For this reason the threshold element assumes an
important role in pattern - classifying machines . We shall use the block diagram
of Fig . 1 .
The sign of g ( x ) can be evaluated by a threshold element whose threshold
value is equal to zero . For this reason the threshold element assumes an
important role in pattern - classifying machines . We shall use the block diagram
of Fig . 1 .
Sivu 21
... Xr are linearly separable , then X1 , X2 , . . . , XR are also pairwise linearly
separable . V2 6 The threshold logic unit ( TLU ) If R = 2 , a linear machine
employs a single linear discriminant function g ( x ) defined by g ( X ) = W1X1 +
W2X2 + .
... Xr are linearly separable , then X1 , X2 , . . . , XR are also pairwise linearly
separable . V2 6 The threshold logic unit ( TLU ) If R = 2 , a linear machine
employs a single linear discriminant function g ( x ) defined by g ( X ) = W1X1 +
W2X2 + .
Sivu 41
4 Winder , R . O . : Threshold Logic in Artificial Intelligence , IEEE Publication S -
142 , Artificial Intelligence ( a combined preprint of papers presented at the winter
general meeting , 1963 ) , pp . 107 – 128 , New York , 1963 . 5 Singleton , R . C ...
4 Winder , R . O . : Threshold Logic in Artificial Intelligence , IEEE Publication S -
142 , Artificial Intelligence ( a combined preprint of papers presented at the winter
general meeting , 1963 ) , pp . 107 – 128 , New York , 1963 . 5 Singleton , R . C ...
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Sisältö
Preface vii | 7 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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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 gi(X 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 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 |
| Alkuperäisteoksen sijainti | Wisconsinin yliopisto - Madison |
| Digitoitu | 16. huhtikuu 2008 |
| ISBN | 0070465703, 9780070465701 |
| Pituus | 137 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |