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 ...
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 ...
Sivu 21
The decision regions R , and R2 are separated by a hyperplane decision surface defined by g ( x ) = 0 . W , Discriminant d * ] g ( X ) = Σ w ; * , + w d +1 1 = 1 : W ; +1 or -1 Response Summing device Threshold element wa xd Pattern d ...
The decision regions R , and R2 are separated by a hyperplane decision surface defined by g ( x ) = 0 . W , Discriminant d * ] g ( X ) = Σ w ; * , + w d +1 1 = 1 : W ; +1 or -1 Response Summing device Threshold element wa xd Pattern d ...
Sivu 48
... function is wi * 1 : Wi g ( x ) F X w d Threshold element d Summing device Pattern w d + 1 +1 Weights FIGURE 3.1 The optimum classifier for binary patterns whose components are statistically independent linear in this case .
... function is wi * 1 : Wi g ( x ) F X w d Threshold element d Summing device Pattern w d + 1 +1 Weights FIGURE 3.1 The optimum classifier for binary patterns whose components are statistically independent linear in this case .
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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 illustrated 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 patterns belonging 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 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 |