Learning Machines: Foundations of Trainable Pattern-classifying Systems |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 26
Sivu 118
Generally , nonparametric training methods are to be preferred to parametric ones because no assumptions need be made about the forms of underlying probability distributions . This advantage is especially im- portant in multimodal ...
Generally , nonparametric training methods are to be preferred to parametric ones because no assumptions need be made about the forms of underlying probability distributions . This advantage is especially im- portant in multimodal ...
Sivu 120
Many of these nonparametric rules actually lead to the same discriminant functions that would be obtained by parametric training and the assumptions that the pattern probability distributions are normal.3 There does exist a simple ...
Many of these nonparametric rules actually lead to the same discriminant functions that would be obtained by parametric training and the assumptions that the pattern probability distributions are normal.3 There does exist a simple ...
Sivu 136
... 76 of of piecewise linear machines , 116 , 122 of TLUS , 65 Nonredundant partition , 107 Normal patterns , 52 , 54 mean vector of , 53 , 54 transformation of , 131 Normal probability - density function , bivariate , 50 equations of ...
... 76 of of piecewise linear machines , 116 , 122 of TLUS , 65 Nonredundant partition , 107 Normal patterns , 52 , 54 mean vector of , 53 , 54 transformation of , 131 Normal probability - density function , bivariate , 50 equations of ...
Mitä ihmiset sanovat - Kirjoita arvostelu
Yhtään arvostelua ei löytynyt.
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
2 muita osia ei näytetty
Muita painoksia - Näytä kaikki
Yleiset termit ja lausekkeet
adjusted apply assume bank called cells changes Chapter cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described dichotomies discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented important initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space Stanford step subsidiary discriminant Suppose 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 |
| ISBN | 0070465703, 9780070465701 |
| Pituus | 137 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |