Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 39
Sivu 18
... Suppose that the components of P ; are Pi1 , Pi2 , Pid . Then the linear machine of Fig . 2.1 is a minimum - distance classifier with respect to the points P1 , P2 , . . . , PR if the weights are given the values and Wij = Pij = = i 1 ...
... Suppose that the components of P ; are Pi1 , Pi2 , Pid . Then the linear machine of Fig . 2.1 is a minimum - distance classifier with respect to the points P1 , P2 , . . . , PR if the weights are given the values and Wij = Pij = = i 1 ...
Sivu 59
... Suppose the pattern vectors belonging to category i are normal with known covariance matrix ; 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 ; 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 , W1 , W2 , • • 9 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 that Z is ...
... suppose that Y1 , Y2 , W1 , W2 , • • 9 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 that Z is ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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assume augmented pattern belonging to category Chapter cluster committee machine committee TLUS correction increment covariance matrix d-dimensional decision surfaces denote diagonal matrix discussed dot products error-correction procedure Euclidean distance example Fix and Hodges function g(X g₁(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 partition pattern classifier pattern hyperplane pattern space pattern vector 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 |