Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 32
Sivu 6
... FIGURE 1.3 Examples of discriminant functions for two - dimensional patterns scalar and single - valued functions of the pattern X. These functions , which we call discriminant functions , are chosen such that for all X in Ri , gi ( X ) ...
... FIGURE 1.3 Examples of discriminant functions for two - dimensional patterns scalar and single - valued functions of the pattern X. These functions , which we call discriminant functions , are chosen such that for all X in Ri , gi ( X ) ...
Sivu 19
... FIGURE 2.2 Examples of decision regions and surfaces resulting from linear discriminant functions S 13 R3 18 R2 R1 1 S 12 S 23 FIGURE 2.3 Decision regions for a minimum - distance classifier with respect to the points P1 , P2 , and P3 ...
... FIGURE 2.2 Examples of decision regions and surfaces resulting from linear discriminant functions S 13 R3 18 R2 R1 1 S 12 S 23 FIGURE 2.3 Decision regions for a minimum - distance classifier with respect to the points P1 , P2 , and P3 ...
Sivu 36
... FIGURE 2.11 An illustration of the construction used in the text for K = 2 , N = 3 , d = 3 We now construct a set of N distinct K - dimensional hyperplanes , each containing Z and one of the points in X. Figure 2.11 illustrates this ...
... FIGURE 2.11 An illustration of the construction used in the text for K = 2 , N = 3 , d = 3 We now construct a set of N distinct K - dimensional hyperplanes , each containing Z and one of the points in X. Figure 2.11 illustrates this ...
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 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 |