Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 48
13 ) The reader will recognize that the optimum discriminant function is 11 - pi +
log T = P ( 1 ) ] g ( x ) Threshold element | Summing device Pattern 10 + 1 + 1
Weights FIGURE 3 . 1 The optimum classifier for binary patterns whose
components ...
13 ) The reader will recognize that the optimum discriminant function is 11 - pi +
log T = P ( 1 ) ] g ( x ) Threshold element | Summing device Pattern 10 + 1 + 1
Weights FIGURE 3 . 1 The optimum classifier for binary patterns whose
components ...
Sivu 55
A set of normal patterns would then tend to be grouped in an ellipsoidal cluster
centered around a prototype pattern M . 3 : 8 The optimum classifier for normal
patterns We are now ready to derive the optimum classifier for normal patterns .
A set of normal patterns would then tend to be grouped in an ellipsoidal cluster
centered around a prototype pattern M . 3 : 8 The optimum classifier for normal
patterns We are now ready to derive the optimum classifier for normal patterns .
Sivu 119
It should be observed that if the two density functions overlap sufficiently , it is
likely that this optimum decision surface will not perfectly separate all the
members of the two training subsets . If we were willing to assume initially that
these ...
It should be observed that if the two density functions overlap sufficiently , it is
likely that this optimum decision surface will not perfectly separate all the
members of the two training subsets . If we were willing to assume initially that
these ...
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Sisältö
Preface vii | 1 |
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 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 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
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 |
| Pituus | 137 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |