Learning Machines: Foundations of Trainable Pattern-classifying Systems |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 27
Sivu 3
First , however , a few words are in order about a key assumption under- * We assume that the response is a ... Pattern classifiers with random responses have been discussed oc- casionally in the literature , but our treatment shall not ...
First , however , a few words are in order about a key assumption under- * We assume that the response is a ... Pattern classifiers with random responses have been discussed oc- casionally in the literature , but our treatment shall not ...
Sivu 72
Note that an adjustment to correct the response for one pattern may very well undo a correction made on a previous pattern . Eventually , however , one last correction will be made that does not affect the correct response to the other ...
Note that an adjustment to correct the response for one pattern may very well undo a correction made on a previous pattern . Eventually , however , one last correction will be made that does not affect the correct response to the other ...
Sivu 98
Therefore these three weight vectors can be employed by a committee of three TLUs in such a Y1 W1 2 Y3 W2 WS FIGURE 6.3 A commmittee of weight vectors way that the consensus or majority of the TLU responses is correct for each pattern .
Therefore these three weight vectors can be employed by a committee of three TLUs in such a Y1 W1 2 Y3 W2 WS FIGURE 6.3 A commmittee of weight vectors way that the consensus or majority of the TLU responses is correct for each pattern .
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 |