Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Tulokset 1 - 3 kokonaismäärästä 13
Sivu 104
Thus , each point in the pattern space is transformed into one of the vertices of a
Pi - dimensional hypercube . This hypercube we shall call the first image space
or the I , space . The transformation between the pattern space and the Il space ...
Thus , each point in the pattern space is transformed into one of the vertices of a
Pi - dimensional hypercube . This hypercube we shall call the first image space
or the I , space . The transformation between the pattern space and the Il space ...
Sivu 105
7 TLU 1 ( a ) Pattern space ( 6 ) Image space FIGURE 6 · 6 Pattern - space to
image - space transformation numbers 1 , 2 , and 3 , we have an easy means of
determining the transformation from the pattern space to the image space .
7 TLU 1 ( a ) Pattern space ( 6 ) Image space FIGURE 6 · 6 Pattern - space to
image - space transformation numbers 1 , 2 , and 3 , we have an easy means of
determining the transformation from the pattern space to the image space .
Sivu 131
A . 3 Transformation of normal patterns Consider the normal distribution
expressed by 1 P ( X ) = ( 2 - ) d12514 exp { - 42 ( X – M ) ' - ( X – M ) ] } ( A . 10 )
where I is the covariance matrix and M is the mean vector . From Eq . ( A : 6 ) we
can write ...
A . 3 Transformation of normal patterns Consider the normal distribution
expressed by 1 P ( X ) = ( 2 - ) d12514 exp { - 42 ( X – M ) ' - ( X – M ) ] } ( A . 10 )
where I is the covariance matrix and M is the mean vector . From Eq . ( A : 6 ) we
can write ...
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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 |