Learning Machines: Foundations of Trainable Pattern-classifying Systems |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 50
Sivu 9
The exact values of the coordinates of the points X and X , are not known , however . If they were known , it might be reasonable for the pattern classifier to divide the pattern space into two regions R , and R2 by some simple decision ...
The exact values of the coordinates of the points X and X , are not known , however . If they were known , it might be reasonable for the pattern classifier to divide the pattern space into two regions R , and R2 by some simple decision ...
Sivu 44
We assume that the p ( Xi ) 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 ( Xi ) , i = 1 , ... , R , are normal probability - density ...
We assume that the p ( Xi ) 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 ( Xi ) , i = 1 , ... , R , are normal probability - density ...
Sivu 49
values are given by Pi ( 1 Wi = i = 1 , d 9 ) log gi ( 1 – pi ) ) log p ( 1 ) + 1 P ( 1 ) ( 3.14 ) W2 + 1 ... Note , for example , that the values of the a priori probabilities p ( 1 ) and 1 – p ( 1 ) affect only the value of wd + 1 .
values are given by Pi ( 1 Wi = i = 1 , d 9 ) log gi ( 1 – pi ) ) log p ( 1 ) + 1 P ( 1 ) ( 3.14 ) W2 + 1 ... Note , for example , that the values of the a priori probabilities p ( 1 ) and 1 – p ( 1 ) affect only the value of wd + 1 .
Mitä ihmiset sanovat - Kirjoita arvostelu
Yhtään arvostelua ei löytynyt.
Sisältö
Preface vii | 11 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
APPENDIX | 127 |
Tekijänoikeudet | |
1 muita osia ei näytetty
Muita painoksia - Näytä kaikki
Yleiset termit ja lausekkeet
adjusted apply assume bank belonging to category called changes Chapter 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 given implemented important initial known layered machine linear dichotomies linear machine linearly separable measurements negative networks 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 selection 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 |