Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 41
... parameters ( for example , cluster points ) . The values of these parameters might be unknown a priori . If the parameters were known , we assume that discriminant functions based on them could have been readily specified . Parametric ...
... parameters ( for example , cluster points ) . The values of these parameters might be unknown a priori . If the parameters were known , we assume that discriminant functions based on them could have been readily specified . Parametric ...
Sivu 43
... parameters ( for example , cluster points ) . The values of these parameters might be unknown a priori . If the parameters were known , we assume that discriminant functions based on them could have been readily specified . Parametric ...
... parameters ( for example , cluster points ) . The values of these parameters might be unknown a priori . If the parameters were known , we assume that discriminant functions based on them could have been readily specified . Parametric ...
Sivu 44
... parameters , whose values might also be unknown , are the a priori probabilities for each class p ( i ) , i = 1 ... parameters of the function p ( Xi ) . The parametric training method for the design of discriminant functions then ...
... parameters , whose values might also be unknown , are the a priori probabilities for each class p ( i ) , i = 1 ... parameters of the function p ( Xi ) . The parametric training method for the design of discriminant functions then ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
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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 |