Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 16
A particular function belonging to this family can be selected by choosing the appropriate values of the parameters . The training of a machine restricted to employ discriminant functions belonging to a particular family can then be ...
A particular function belonging to this family can be selected by choosing the appropriate values of the parameters . The training of a machine restricted to employ discriminant functions belonging to a particular family can then be ...
Sivu 44
We assume that the p ( X ) are known functions of a finite number of characteristic parameters whose values we might not know a priori . For example , we may know that the p ( X | i ) , i = 1 , . . . , R , are normal probability ...
We assume that the p ( X ) are known functions of a finite number of characteristic parameters whose values we might not know a priori . For example , we may know that the p ( X | i ) , i = 1 , . . . , R , are normal probability ...
Sivu 49
values are given by Pi Wi = log [ 2 : ( 1-2 ) ] gi ) i = pi ) Wa + 1 = log p ( 1 ) - p ( 1 ) 1 , 1 · - Pi " Σ [ 1 ] ... Note , for example , that the values of the a priori proba- bilities p ( 1 ) and 1 p ( 1 ) affect only the value of wa ...
values are given by Pi Wi = log [ 2 : ( 1-2 ) ] gi ) i = pi ) Wa + 1 = log p ( 1 ) - p ( 1 ) 1 , 1 · - Pi " Σ [ 1 ] ... Note , for example , that the values of the a priori proba- bilities p ( 1 ) and 1 p ( 1 ) affect only the value of wa ...
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Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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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 |