Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 23
Sivu 96
... layers have as their inputs the outputs of the x2 X : Id TLUS- Response Pattern FIGURE 6.1 A network of TLUS TLUS in the preceding layer only . The output of the single TLU in the final layer is the response of the machine . Layered ...
... layers have as their inputs the outputs of the x2 X : Id TLUS- Response Pattern FIGURE 6.1 A network of TLUS TLUS in the preceding layer only . The output of the single TLU in the final layer is the response of the machine . Layered ...
Sivu 97
... machines can be trained by varying the weights associated with each TLU in the network . There do not yet exist , how- ever , efficient adjustment rules for such thorough training of a layered machine . In the next three sections , we ...
... machines can be trained by varying the weights associated with each TLU in the network . There do not yet exist , how- ever , efficient adjustment rules for such thorough training of a layered machine . In the next three sections , we ...
Sivu 109
... layered machines are piecewise linear . In this section , we shall verify this statement . Y Consider the first layer of a layered machine for LAYERED MACHINES 109 Derivation of a discriminant function for a layered machine,
... layered machines are piecewise linear . In this section , we shall verify this statement . Y Consider the first layer of a layered machine for LAYERED MACHINES 109 Derivation of a discriminant function for a layered machine,
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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adjusted apply assume bank called cells changes Chapter cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described dichotomies discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented important initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors X1 and X2 Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |
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 |
| ISBN | 0070465703, 9780070465701 |
| Pituus | 137 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |