Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 9
If the values of these parameters were known , adequate discriminant functions based on them could be directly specified . ... estimates of the parameter values , and the discriminant functions are then determined by these estimates .
If the values of these parameters were known , adequate discriminant functions based on them could be directly specified . ... estimates of the parameter values , and the discriminant functions are then determined by these estimates .
Sivu 44
We assume that the p ( X \ i ) 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 ( Xlc ) , i = 1 , ... , R , are normal probability ...
We assume that the p ( X \ i ) 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 ( Xlc ) , i = 1 , ... , R , are normal probability ...
Sivu 49
values are given by pi ( 1 qi ) Wi = į = 1 , d Ji ( 1 Pi ) log [ % : = ] log [ . ... Note , for example , that the values of the a priori probabilities p ( 1 ) and 1 – p ( 1 ) affect only the value of wd + 1 . As category 1 becomes less ...
values are given by pi ( 1 qi ) Wi = į = 1 , d Ji ( 1 Pi ) log [ % : = ] log [ . ... Note , for example , that the values of the a priori probabilities p ( 1 ) and 1 – p ( 1 ) affect only the value of wd + 1 . As category 1 becomes less ...
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adjusted apply assume bank belonging to category called changes Chapter classifier cluster committee components consider consists contains correction corresponding decision surfaces define denote density depends derivation described discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed gi(X given illustrated implemented important initial known layered machine linear dichotomies linear machine linearly separable negative normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem proof properties proved PWL machine quadric reduced regions respect response rule sample mean selected separable shown side space specific Stanford step Suppose theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors 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 |