Learning Machines: Foundations of Trainable Pattern-classifying Systems |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 20
Sivu 53
center of gravity of the cluster approach the mean values mı , m2 of the corresponding normal distribution . The notation used in Eq . ( 3.20 ) to describe the normal distribution can be made more compact if we define and use the ...
center of gravity of the cluster approach the mean values mı , m2 of the corresponding normal distribution . The notation used in Eq . ( 3.20 ) to describe the normal distribution can be made more compact if we define and use the ...
Sivu 54
3 : 7 The multivariate normal distribution The matrix notation of the last section has a direct parallel in the case in which the number of pattern components is greater than two . There is a multivariate normal distribution which ...
3 : 7 The multivariate normal distribution The matrix notation of the last section has a direct parallel in the case in which the number of pattern components is greater than two . There is a multivariate normal distribution which ...
Sivu 118
discriminant functions while leaving their distributions within the banks fixed . Training patterns are presented to the PWL machine whose R banks of subsidiary linear discriminant functions have initially been selected arbitrarily .
discriminant functions while leaving their distributions within the banks fixed . Training patterns are presented to the PWL machine whose R banks of subsidiary linear discriminant functions have initially been selected arbitrarily .
Mitä ihmiset sanovat - Kirjoita arvostelu
Yhtään arvostelua ei löytynyt.
Sisältö
I | 1 |
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 covariance 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 layered machine linear dichotomies linear machine linearly separable matrix measurements 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 selection separable shown side solution 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 |