Learning Machines: Foundations of Trainable Pattern-classifying Systems |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 31
Sivu 19
S 13 S R1 $ 14 34 Rs RA S1 , ( redundant ) 12 R2 S 23 24 FIGURE 2.2 Examples of decision regions and surfaces resulting from linear discriminant functions S 13 R3 R2 1 S 12 S 23 FIGURE 2.3 Decision regions for a minimum - distance ...
S 13 S R1 $ 14 34 Rs RA S1 , ( redundant ) 12 R2 S 23 24 FIGURE 2.2 Examples of decision regions and surfaces resulting from linear discriminant functions S 13 R3 R2 1 S 12 S 23 FIGURE 2.3 Decision regions for a minimum - distance ...
Sivu 68
The A solution weight point W , Solution region 2 3 w Pattern hyperplanes FIGURE 4.1 A two - dimensional weight space with three pattern hyperplanes encircled numbers attached to the hyperplanes indicate the number of the pattern .
The A solution weight point W , Solution region 2 3 w Pattern hyperplanes FIGURE 4.1 A two - dimensional weight space with three pattern hyperplanes encircled numbers attached to the hyperplanes indicate the number of the pattern .
Sivu 73
We note that termination occurs during the x1 1,0,1 1,0,0 0,0,1 , 0,1,1 A separating plane 0,0,0 0,1,0 1,1,0 O Patterns requiring -1 response Patterns requiring +1 response FIGURE 4.3 A plane which correctly partitions eight three ...
We note that termination occurs during the x1 1,0,1 1,0,0 0,0,1 , 0,1,1 A separating plane 0,0,0 0,1,0 1,1,0 O Patterns requiring -1 response Patterns requiring +1 response FIGURE 4.3 A plane which correctly partitions eight three ...
Mitä ihmiset sanovat - Kirjoita arvostelu
Yhtään arvostelua ei löytynyt.
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
3 muita osia ei näytetty
Muita painoksia - Näytä kaikki
Yleiset termit ja lausekkeet
adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern hyperplane pattern space pattern vector pattern-classifying piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space step subsidiary discriminant Suppose terns 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 |