Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 3
We assume that the response is a deterministic ( nonrandom ) function of the
input pattern . Pattern classifiers with random responses have been discussed
occasionally in the literature , but our treatment shall not include them .
We assume that the response is a deterministic ( nonrandom ) function of the
input pattern . Pattern classifiers with random responses have been discussed
occasionally in the literature , but our treatment shall not include them .
Sivu 72
There are four patterns represented by pattern hyperplanes in weight space . The
small arrows attached to these planes in this case indicate the side on which a
TLU weight vector will give the desired response . The patterns will be presented
...
There are four patterns represented by pattern hyperplanes in weight space . The
small arrows attached to these planes in this case indicate the side on which a
TLU weight vector will give the desired response . The patterns will be presented
...
Sivu 98
3 A commmittee of weight vectors way that the consensus or majority of the TLU
responses is correct for each pattern . ... The second layer consists of the vote -
taking TLU whose response is the majority response of the committee TLUs .
3 A commmittee of weight vectors way that the consensus or majority of the TLU
responses is correct for each pattern . ... The second layer consists of the vote -
taking TLU whose response is the majority response of the committee TLUs .
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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 |