Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Tulokset 1 - 3 kokonaismäärästä 25
Sivu 23
where || = 1 Σ ω ; · 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 || = 1 Σ ω ; · 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 by P2 ( M + 1 ) , M = 1/2 ( 2 · 45 ) The threshold effect around 2 ( M + 1 ) can be expressed quantitively lim P ( 2+ ) ( M + 1 ) , M = 0 for alle > 0 M → ∞ and lim P ( 2 ...
Also note that for each value of M by P2 ( M + 1 ) , M = 1/2 ( 2 · 45 ) The threshold effect around 2 ( M + 1 ) can be expressed quantitively lim P ( 2+ ) ( M + 1 ) , M = 0 for alle > 0 M → ∞ and lim P ( 2 ...
Sivu 49
Note , for example , that the values of the a priori proba- bilities p ( 1 ) and 1 p ( 1 ) affect only the value of wa + 1 . As category 1 becomes less probable a priori , wa + 1 decreases . Such a decrease of wa + 1 favors a category 2 ...
Note , for example , that the values of the a priori proba- bilities p ( 1 ) and 1 p ( 1 ) affect only the value of wa + 1 . As category 1 becomes less probable a priori , wa + 1 decreases . Such a decrease of wa + 1 favors a category 2 ...
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Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern hyperplane pattern space pattern vector pattern-classifying piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space step subsidiary discriminant Suppose terns 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 |
| Alkuperäisteoksen sijainti | Wisconsinin yliopisto - Madison |
| Digitoitu | 16. huhtikuu 2008 |
| ISBN | 0070465703, 9780070465701 |
| Pituus | 137 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |