Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 34
Sivu 69
... pattern Y in y , a TLU with a weight vector W has a response which is either erroneous ( Y. W < 0 ) or undefined ( Y ⋅ W = 0 ) . That is , W is either on the negative side of or on the pattern hyperplane corresponding to Y. This error ...
... pattern Y in y , a TLU with a weight vector W has a response which is either erroneous ( Y. W < 0 ) or undefined ( Y ⋅ W = 0 ) . That is , W is either on the negative side of or on the pattern hyperplane corresponding to Y. This error ...
Sivu 71
... pattern hyperplane is always the same . This fixed distance . may or may not be sufficient to cross the pattern hyperplane and thus correct the error . In another case , c is chosen to be just large enough to Initial weight point Final ...
... pattern hyperplane is always the same . This fixed distance . may or may not be sufficient to cross the pattern hyperplane and thus correct the error . In another case , c is chosen to be just large enough to Initial weight point Final ...
Sivu 102
... pattern hyperplane are adjusted by the addi- tion of the pattern vector . Consider the three pattern vectors and their corresponding pattern hyperplanes ( lines ) shown in Fig . 6 · 5 . The arrows indicate the positive sides of the ...
... pattern hyperplane are adjusted by the addi- tion of the pattern vector . Consider the three pattern vectors and their corresponding pattern hyperplanes ( lines ) shown in Fig . 6 · 5 . The arrows indicate the positive sides of the ...
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 |