Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 8
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
1 1 ' j = 1 → + 1 or - 1 x : Response / Summing device Threshold element Pattern
wa + 1 + 1 Weights FIGURE 2 . 4 The threshold logic unit ( TLU ) The pattern
dichotomizer with linear g ( x ) can be implemented according to the block
diagram ...
1 1 ' j = 1 → + 1 or - 1 x : Response / Summing device Threshold element Pattern
wa + 1 + 1 Weights FIGURE 2 . 4 The threshold logic unit ( TLU ) The pattern
dichotomizer with linear g ( x ) can be implemented according to the block
diagram ...
Sivu 48
13 ) The reader will recognize that the optimum discriminant function is w1
Threshold element Summing device Pattern wd + 1 + 1 - Weights FIGURE 3 . 1
The optimum classifier for binary patterns whose components are statistically ...
13 ) The reader will recognize that the optimum discriminant function is w1
Threshold element Summing device Pattern wd + 1 + 1 - Weights FIGURE 3 . 1
The optimum classifier for binary patterns whose components are statistically ...
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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 |