Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 17
Sivu 3
... lines and one output line ( see Fig . 1.1 ) . The d input lines are activated simultaneously by the pattern , and the output line responds * * 1 x ; xd Pattern ( data to be classified ) Response ( classification ) = 1 or 2 or 3 or ...
... lines and one output line ( see Fig . 1.1 ) . The d input lines are activated simultaneously by the pattern , and the output line responds * * 1 x ; xd Pattern ( data to be classified ) Response ( classification ) = 1 or 2 or 3 or ...
Sivu 34
... lines . Because the members of X are in general position , each of these lines is distinct ( i.e. , no three points of X are on the same line ) . Select some hyperplane H having an intersection with each of these lines and let these ...
... lines . Because the members of X are in general position , each of these lines is distinct ( i.e. , no three points of X are on the same line ) . Select some hyperplane H having an intersection with each of these lines and let these ...
Sivu 97
... lines point toward the posi- tive sides of the lines . These lines divide the weight space into six regions ; thus , there are six linear dichotomies of three one - dimensional patterns . But there are 23 8 different possible ...
... lines point toward the posi- tive sides of the lines . These lines divide the weight space into six regions ; thus , there are six linear dichotomies of three one - dimensional patterns . But there are 23 8 different possible ...
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 |