Learning Machines: Foundations of Trainable Pattern-classifying Systems |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 36
Sivu 28
0 equal to zero only for X = 1 J . When these conditions are met , both 0 the matrix
A and the quadratic form are called positive definite . If A has one or more of its
eigenvalues equal to zero and all the others positive , then the quadratic form will
...
0 equal to zero only for X = 1 J . When these conditions are met , both 0 the matrix
A and the quadratic form are called positive definite . If A has one or more of its
eigenvalues equal to zero and all the others positive , then the quadratic form will
...
Sivu 54
Patterns selected according to this joint probability distribution will be called
multivariate normal patterns or , more simply , normal patterns . The expression
for the d - variate normal probability distribution is almost identical in form to that
of Eq ...
Patterns selected according to this joint probability distribution will be called
multivariate normal patterns or , more simply , normal patterns . The expression
for the d - variate normal probability distribution is almost identical in form to that
of Eq ...
Sivu 69
4 ) where c is a positive number called the correction increment . It controls the
extent of the adjustment . For sufficiently large c , the weight point will cross the
pattern hyperplane , and Y : W ' will be correctly positive . If W were incorrectly on
...
4 ) where c is a positive number called the correction increment . It controls the
extent of the adjustment . For sufficiently large c , the weight point will cross the
pattern hyperplane , and Y : W ' will be correctly positive . If W were incorrectly on
...
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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 |