Learning Machines: Foundations of Trainable Pattern-classifying Systems |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 35
Sivu 19
S 13 R1 S 34 R3 R4 R2 S 524 23 14 S1 , ( redundant ) 12 FIGURE 2.2 Examples of decision regions and surfaces resulting from linear discriminant functions S 13 R3 R2 R1 S 12 S 23 FIGURE 2.3 Decision regions for a minimum - distance ...
S 13 R1 S 34 R3 R4 R2 S 524 23 14 S1 , ( redundant ) 12 FIGURE 2.2 Examples of decision regions and surfaces resulting from linear discriminant functions S 13 R3 R2 R1 S 12 S 23 FIGURE 2.3 Decision regions for a minimum - distance ...
Sivu 96
The TLUS in the second and subsequent layers have as their inputs the outputs of the x2 X : Id TLUS- Response Pattern FIGURE 6.1 A network of TLUS TLUS in the preceding layer only . The output of the single TLU in the final layer is the ...
The TLUS in the second and subsequent layers have as their inputs the outputs of the x2 X : Id TLUS- Response Pattern FIGURE 6.1 A network of TLUS TLUS in the preceding layer only . The output of the single TLU in the final layer is the ...
Sivu 106
In this figure the points marked repre- sent patterns belonging to X1 , and the points marked O represent pat- terns belonging to X2 . Clearly the TLUs in the first layer of the desired layered machine must at least implement ...
In this figure the points marked repre- sent patterns belonging to X1 , and the points marked O represent pat- terns belonging to X2 . Clearly the TLUs in the first layer of the desired layered machine must at least implement ...
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 |