Learning Machines: Foundations of Trainable Pattern-classifying Systems |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 24
Sivu 18
An equivalent classification is obtained by comparing the squared distances XP . ... P ; ( 2 · 4 ) The minimum - distance classification can be effected by comparing the expressions X. Pi1⁄2P ; P ; for i = 1 , . . . , R and selecting ...
An equivalent classification is obtained by comparing the squared distances XP . ... P ; ( 2 · 4 ) The minimum - distance classification can be effected by comparing the expressions X. Pi1⁄2P ; P ; for i = 1 , . . . , R and selecting ...
Sivu 24
Let us define the Euclidean distance d ( X , P , ) from an arbi- trary point X to the point set P ; by d ( X ... ( 2 · 16 ) That is , the distance between X and P , is the smallest of the distances between X and each point in P. Let us ...
Let us define the Euclidean distance d ( X , P , ) from an arbi- trary point X to the point set P ; by d ( X ... ( 2 · 16 ) That is , the distance between X and P , is the smallest of the distances between X and each point in P. Let us ...
Sivu 121
To classify any pat- tern X , the distance between X and each of the patterns in the training subsets must be computed . If these computations are to be performed rapidly , each of the training patterns must be stored ( as weight ...
To classify any pat- tern X , the distance between X and each of the patterns in the training subsets must be computed . If these computations are to be performed rapidly , each of the training patterns must be stored ( as weight ...
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 |