Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 26
Sivu 87
... training theorem for R - category linear machines Suppose we are given R finite subsets of augmented training pattern vec- tors Y1 , Y2 , ... , YR . These subsets are linearly separable if and only if there exist R solution weight ...
... training theorem for R - category linear machines Suppose we are given R finite subsets of augmented training pattern vec- tors Y1 , Y2 , ... , YR . These subsets are linearly separable if and only if there exist R solution weight ...
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 ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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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 |