Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 33
Sivu 19
... FIGURE 2.2 Examples of decision regions and surfaces resulting from linear discriminant functions S 13 R3 R2 R2 S 12 FIGURE 2.3 S 23 Decision regions for a minimum - distance classifier with respect to the points P1 , P2 , and P3 In ...
... FIGURE 2.2 Examples of decision regions and surfaces resulting from linear discriminant functions S 13 R3 R2 R2 S 12 FIGURE 2.3 S 23 Decision regions for a minimum - distance classifier with respect to the points P1 , P2 , and P3 In ...
Sivu 36
... FIGURE 211 An illustration of the construction used in the text 2 , N = 3 , d for K = = 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 211 An illustration of the construction used in the text 2 , N = 3 , d for K = = 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 ...
Sivu 106
... 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 hyperplanes ...
... 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 hyperplanes ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding decision surfaces define denote density depends derivation described Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial layered machine linear dichotomies linear discriminant functions linear machine linearly separable measurements negative networks normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem proof properties proved PWL machine quadric reduced regions respect response rule sample mean selection separable shown side space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods training procedure training sequence training subsets transformation values weight vectors zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |