Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 40
Sivu 89
... suppose that Y1 , Y2 , W1 , W2 • · · 9 2 , ... YR are linearly separable with a set of solution weight vectors WR ; then observe that Z is linearly contained with an RD- dimensional vector V = ( W1 , W2 , ... , WR ) . Conversely , suppose ...
... suppose that Y1 , Y2 , W1 , W2 • · · 9 2 , ... YR are linearly separable with a set of solution weight vectors WR ; then observe that Z is linearly contained with an RD- dimensional vector V = ( W1 , W2 , ... , WR ) . Conversely , suppose ...
Sivu 92
... Suppose it is not , but instead lies outside * at a distance △ from one of the pattern hyperplanes bounding W. But the pattern corresponding to this hyperplane will eventually occur in Sf , say at the kth step . Suppose k to be ...
... Suppose it is not , but instead lies outside * at a distance △ from one of the pattern hyperplanes bounding W. But the pattern corresponding to this hyperplane will eventually occur in Sf , say at the kth step . Suppose k to be ...
Sivu 123
... Suppose that patterns are presented to the PWL machine one at a time from a training sequence . Let the initial weight vectors be selected arbitrarily . * We shall describe the adjustments to be made at the kth step . Suppose that the ...
... Suppose that patterns are presented to the PWL machine one at a time from a training sequence . Let the initial weight vectors be selected arbitrarily . * We shall describe the adjustments to be made at the kth step . Suppose that the ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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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 step subsidiary discriminant Suppose terns 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 |