Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Tulokset 1 - 3 kokonaismäärästä 9
Sivu 106
By partition we mean that the hyperplanes divide TLU 3 TLU 2 TLU 3 TLU 1 TLU
2 ( a ) Pattern space FIGURE 6 : 7 TLU 1 ... The necessity for partitioning the sets
X1 and X2 arises because corresponding to each nonempty cell in the pattern ...
By partition we mean that the hyperplanes divide TLU 3 TLU 2 TLU 3 TLU 1 TLU
2 ( a ) Pattern space FIGURE 6 : 7 TLU 1 ... The necessity for partitioning the sets
X1 and X2 arises because corresponding to each nonempty cell in the pattern ...
Sivu 107
We know for example that it is necessary that these hyperplanes partition X , and
X2 . A set of conditions that are both necessary and sufficient have not yet been
found , but in the next section we shall state some sufficient conditions .
We know for example that it is necessary that these hyperplanes partition X , and
X2 . A set of conditions that are both necessary and sufficient have not yet been
found , but in the next section we shall state some sufficient conditions .
Sivu 108
partition with the property that if any one of the separating hyperplanes is
removed , at least two nonempty cells will merge into one cell . Some examples
of nonredundant and redundant partitions are shown in Fig . 6 . 8 . Note that the
partition ...
partition with the property that if any one of the separating hyperplanes is
removed , at least two nonempty cells will merge into one cell . Some examples
of nonredundant and redundant partitions are shown in Fig . 6 . 8 . Note that the
partition ...
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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 |