Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 119
... training subsets . If we were willing to assume initially that these distributions were normal , then the parametric training methods outlined in Chapter 3 would lead to a decision surface closely approximating the optimum sur- face if ...
... training subsets . If we were willing to assume initially that these distributions were normal , then the parametric training methods outlined in Chapter 3 would lead to a decision surface closely approximating the optimum sur- face if ...
Sivu 120
... training subsets . Many of these nonparametric rules actually lead to the same discriminant functions that would be obtained by parametric training and the assumptions that the pattern probability distributions are normal.3 There does ...
... training subsets . Many of these nonparametric rules actually lead to the same discriminant functions that would be obtained by parametric training and the assumptions that the pattern probability distributions are normal.3 There does ...
Sivu 121
... training subsets must be computed . If these computations are to be performed rapidly , each of the training patterns must be stored ( as weight vectors , for example ) in some rapid - access memory . Because the method works best when ...
... training subsets must be computed . If these computations are to be performed rapidly , each of the training patterns must be stored ( as weight vectors , for example ) in some rapid - access memory . Because the method works best when ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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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 |