Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 39
Sivu 59
... Suppose the pattern vectors belonging to category i are normal with known covariance matrix Σ ; and unknown mean vector . Thus , the d com- ponents of the mean vector are the only unknown parameters of the dis- criminant function . For ...
... Suppose the pattern vectors belonging to category i are normal with known covariance matrix Σ ; and unknown mean vector . Thus , the d com- ponents of the mean vector are the only unknown parameters of the dis- criminant function . For ...
Sivu 89
... suppose that Y1 , Y2 , ... , YR are linearly separable with a set of solution weight vectors W1 , W2 , ... , WR ; then observe that Z is linearly contained with an RD- dimensional vector V = ( W1 , W2 , , WR ) . Conversely , suppose ...
... suppose that Y1 , Y2 , ... , YR are linearly separable with a set of solution weight vectors W1 , W2 , ... , 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 Sŷ , 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 Sŷ , say at the kth step . Suppose k to be ...
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 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 |