Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Tulokset 1 - 3 kokonaismäärästä 19
Sivu 53
center of gravity of the cluster approach the mean values mi , m2 of the
corresponding normal distribution . The notation used in Eq . ( 3 : 20 ) to describe
the normal distribution can be made more compact if we define and use the
following ...
center of gravity of the cluster approach the mean values mi , m2 of the
corresponding normal distribution . The notation used in Eq . ( 3 : 20 ) to describe
the normal distribution can be made more compact if we define and use the
following ...
Sivu 54
3 : 7 The multivariate normal distribution The matrix notation of the last section
has a direct parallel in the case in which the number of pattern components is
greater than two . There is a multivariate normal distribution which describes the
joint ...
3 : 7 The multivariate normal distribution The matrix notation of the last section
has a direct parallel in the case in which the number of pattern components is
greater than two . There is a multivariate normal distribution which describes the
joint ...
Sivu 118
discriminant functions while leaving their distributions within the banks fixed .
Training patterns are presented to the PWL machine whose R banks of
subsidiary linear discriminant functions have initially been selected arbitrarily .
discriminant functions while leaving their distributions within the banks fixed .
Training patterns are presented to the PWL machine whose R banks of
subsidiary linear discriminant functions have initially been selected arbitrarily .
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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 |