Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Tulokset 1 - 3 kokonaismäärästä 9
Sivu 49
We then presume that these estimates are the true values and use them in Eq . (
3 . ... Thus , Ni + N2 = N . Reasonable * estimates for the unknown probabilities
might then be : number of typical patterns belonging to. * The reader with ...
We then presume that these estimates are the true values and use them in Eq . (
3 . ... Thus , Ni + N2 = N . Reasonable * estimates for the unknown probabilities
might then be : number of typical patterns belonging to. * The reader with ...
Sivu 58
The ( X ) i and ( 2 ) i are reasonable * estimates of Miand Ei , respectively . The
use of these estimates to specify the discriminant functions would constitute a
parametric training method . An expression that is somewhat simpler than the
one in ...
The ( X ) i and ( 2 ) i are reasonable * estimates of Miand Ei , respectively . The
use of these estimates to specify the discriminant functions would constitute a
parametric training method . An expression that is somewhat simpler than the
one in ...
Sivu 120
The Fix and Hodges procedure clearly is an attempt to estimate the values of p (
X \ i ) p ( i ) for i = 1 , . . . , R around the ... the training subsets are small , the
estimates ni , n2 , . . . , ne will not be good ones ( neither would any other
estimates ) .
The Fix and Hodges procedure clearly is an attempt to estimate the values of p (
X \ i ) p ( i ) for i = 1 , . . . , R around the ... the training subsets are small , the
estimates ni , n2 , . . . , ne will not be good ones ( neither would any other
estimates ) .
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Sisältö
Preface vii | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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Yleiset termit ja lausekkeet
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 |