Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 18
Sivu 32
... dichotomies of N patterns that its members could effect . We shall show that if the positions of the N pattern points satisfy some quite mild conditions , the number of dichotomies that can be ... linear dichotomies of N points dimensions, 1.
... dichotomies of N patterns that its members could effect . We shall show that if the positions of the N pattern points satisfy some quite mild conditions , the number of dichotomies that can be ... linear dichotomies of N points dimensions, 1.
Sivu 33
... linear dichotomies of X ' . We wish to find out by how much this number of linear dichotomies is increased if the set X ' is enlarged to - " X ,. 2 X 2 X , 3 4 2 3 4 le 5 6x3 X3 1 ls ( a ) Points in general position ( b ) Three points ...
... linear dichotomies of X ' . We wish to find out by how much this number of linear dichotomies is increased if the set X ' is enlarged to - " X ,. 2 X 2 X , 3 4 2 3 4 le 5 6x3 X3 1 ls ( a ) Points in general position ( b ) Three points ...
Sivu 97
... linear dichotomies of three one - dimensional patterns . But there are 23 8 different possible dichotomies of three patterns ; therefore two of the dichotomies are not linear . As an example of a non- linear dichotomy , consider the one ...
... linear dichotomies of three one - dimensional patterns . But there are 23 8 different possible dichotomies of three patterns ; therefore two of the dichotomies are not linear . As an example of a non- linear dichotomy , consider the one ...
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 |