Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 40
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 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 ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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assume augmented pattern belonging to category Chapter cluster committee machine committee TLUS components correction increment covariance matrix d-dimensional decision surfaces denote diagonal matrix discussed dot products error-correction procedure Euclidean distance example Fix and Hodges gi(X given Hodges method hypersphere image-space implemented initial weight vectors ith bank layer of TLUS layered machine linear dichotomies linear discriminant functions linearly separable loss function mean vector minimum-distance classifier mode-seeking networks nonparametric number of patterns p₁ parameters parametric training partition pattern hyperplane pattern points pattern space pattern vector pattern-classifying patterns belonging perceptron piecewise linear plane point sets positive probability distributions prototype pattern PWL machine quadratic form quadric function rule sample covariance matrix shown in Fig solution weight vectors Stanford subsets X1 subsidiary discriminant functions Suppose terns TLU response training patterns training sequence training set training subsets transformation two-layer machine values W₁ weight point weight space weight-vector sequence X1 and X2 zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |