Learning Machines: Foundations of Trainable Pattern-classifying Systems |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 43
Sivu 103
At the next stage , examining the weight - vector positions with respect to the Y2 pattern hyperplane we see that all of ... Later , another adjustment for Y3 results in a satisfactory location of the three committee weight vectors .
At the next stage , examining the weight - vector positions with respect to the Y2 pattern hyperplane we see that all of ... Later , another adjustment for Y3 results in a satisfactory location of the three committee weight vectors .
Sivu 117
What is needed , then , to train PWL machines is a method of adjust- ing weight vectors and a method for transferring them from bank to bank . Preferably , these training procedures should be iterative so that compli- cated analyses of ...
What is needed , then , to train PWL machines is a method of adjust- ing weight vectors and a method for transferring them from bank to bank . Preferably , these training procedures should be iterative so that compli- cated analyses of ...
Sivu 122
L. Thus there are R banks of weight vectors , and the ith bank has Li members . Any training method which adjusts the weight vectors in each bank so that each weight vector finally resides in the center of a cluster of like - category ...
L. Thus there are R banks of weight vectors , and the ith bank has Li members . Any training method which adjusts the weight vectors in each bank so that each weight vector finally resides in the center of a cluster of like - category ...
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 |