Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Note that the decision surfaces are segments of hyperplanes ( lines for d = 2 ) , and that S12 is redundant . In the special case in which the linear machine is a minimum - distance classi- fier , the surface Si ; is the hyperplane ...
Note that the decision surfaces are segments of hyperplanes ( lines for d = 2 ) , and that S12 is redundant . In the special case in which the linear machine is a minimum - distance classi- fier , the surface Si ; is the hyperplane ...
Sivu 23
where d | w│ = Σ ω ; 2 Note from Fig . 2.5 that the absolute value of n⚫ P is the normal Euclidean distance from the origin to the hyperplane . We shall denote this distance by the symbol Aw , which we set equal to wa + 1 / w ) .
where d | w│ = Σ ω ; 2 Note from Fig . 2.5 that the absolute value of n⚫ P is the normal Euclidean distance from the origin to the hyperplane . We shall denote this distance by the symbol Aw , which we set equal to wa + 1 / w ) .
Sivu 39
Also note that for each value of M P2 ( M + 1 ) , M = 1/2 ( 2.45 ) The threshold effect around 2 ( M + 1 ) can be expressed quantitively by lim P ( 2+ ) ( M + 1 ) , M = 0 for alle > 0 M → ∞ and lim P ( 2 − e ) ( M ...
Also note that for each value of M P2 ( M + 1 ) , M = 1/2 ( 2.45 ) The threshold effect around 2 ( M + 1 ) can be expressed quantitively by lim P ( 2+ ) ( M + 1 ) , M = 0 for alle > 0 M → ∞ and lim P ( 2 − e ) ( M ...
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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 |