Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 103
... Transformation properties of layered machines We have seen in Secs . 6.2 to 6-4 that the concept of the first - layer TLUS as voters in a " committee " is a productive representation for two - layer machines . Another representation ...
... Transformation properties of layered machines We have seen in Secs . 6.2 to 6-4 that the concept of the first - layer TLUS as voters in a " committee " is a productive representation for two - layer machines . Another representation ...
Sivu 104
... transform each of the I1 - space vertices into one of the vertices of a P2 - dimensional hypercube . The transformation from I1 space to I2 space depends on the values of the weights in the second layer . For a given set of weights ...
... transform each of the I1 - space vertices into one of the vertices of a P2 - dimensional hypercube . The transformation from I1 space to I2 space depends on the values of the weights in the second layer . For a given set of weights ...
Sivu 131
... transformation where Using Eq . ( A - 14 ) we have Ꮓ = QX Q = DtTt ( A.13 ) ( A ∙ 14 ) 1 p ( X ) = ( 2π ) d / 22 exp [ -12 ( Z - QM ) ( Z — QM ) ] - ( A.15 ) Equation ( A - 15 ) suggests that the vector Z = QX is a normal pat- tern ...
... transformation where Using Eq . ( A - 14 ) we have Ꮓ = QX Q = DtTt ( A.13 ) ( A ∙ 14 ) 1 p ( X ) = ( 2π ) d / 22 exp [ -12 ( Z - QM ) ( Z — QM ) ] - ( A.15 ) Equation ( A - 15 ) suggests that the vector Z = QX is a normal pat- tern ...
Sisältö
Preface vii | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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assume belonging to category Chapter cluster committee machine committee TLUS components correction increment covariance matrix 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 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 training patterns training sequence training set training subsets transformation two-layer machine values W₁ wa+1 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 |