Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 49
14 ) in some limiting cases will show that the discriminant function depends in a
reasonable way on the probabilities involved . Note , for example , that the values
of the a priori probabilities p ( 1 ) and 1 – p ( 1 ) affect only the value of Wd41 ...
14 ) in some limiting cases will show that the discriminant function depends in a
reasonable way on the probabilities involved . Note , for example , that the values
of the a priori probabilities p ( 1 ) and 1 – p ( 1 ) affect only the value of Wd41 ...
Sivu 54
There is a multivariate normal distribution which describes the joint probability
density of d components . Patterns selected according to this joint probability
distribution will be called multivariate normal patterns or , more simply , normal ...
There is a multivariate normal distribution which describes the joint probability
density of d components . Patterns selected according to this joint probability
distribution will be called multivariate normal patterns or , more simply , normal ...
Sivu 136
... 75 of 0 machines , 76 of piecewise linear machines , 116 , 122 of TLUs , 65
Nonredundant partition , 107 Normal patterns , 52 , 54 mean vector of , 53 , 54
transformation of , 131 Normal probability - density function , bivariate , 50
equations ...
... 75 of 0 machines , 76 of piecewise linear machines , 116 , 122 of TLUs , 65
Nonredundant partition , 107 Normal patterns , 52 , 54 mean vector of , 53 , 54
transformation of , 131 Normal probability - density function , bivariate , 50
equations ...
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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 |