Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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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 ...
Sivu 120
function that depends on the geometric arrangement of the patterns in the
training subsets . Many of these nonparametric rules actually lead to the same
discriminant functions that would be obtained by parametric training and the
assumptions ...
function that depends on the geometric arrangement of the patterns in the
training subsets . Many of these nonparametric rules actually lead to the same
discriminant functions that would be obtained by parametric training and the
assumptions ...
Sivu 121
if N is the total number of patterns in the training subsets . The value of k / N ,
however , should decrease toward zero with increasing N . The high storage
requirements of the Fix and Hodges method render it impractical in most pattern ...
if N is the total number of patterns in the training subsets . The value of k / N ,
however , should decrease toward zero with increasing N . The high storage
requirements of the Fix and Hodges method render it impractical in most pattern ...
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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 |