Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 26
Sivu 5
... consider the sets shown in Fig . 1.2 where d 2 and R = 3. A point in the plane is mapped into the numbers 1 , 2 , or 3 , according to its membership in R1 , R2 , or R3 , respectively . For example , the pattern ( 5 , -3 ) would be ...
... consider the sets shown in Fig . 1.2 where d 2 and R = 3. A point in the plane is mapped into the numbers 1 , 2 , or 3 , according to its membership in R1 , R2 , or R3 , respectively . For example , the pattern ( 5 , -3 ) would be ...
Sivu 24
... consider those of a minimum - distance classifier with respect to point sets . i = 1 ,. Suppose we are given R finite point sets P1 , P2 , . . . , PR . For each R , let the ith point set consist of the L points P ( 1 ) , P , ( 2 ) , P ...
... consider those of a minimum - distance classifier with respect to point sets . i = 1 ,. Suppose we are given R finite point sets P1 , P2 , . . . , PR . For each R , let the ith point set consist of the L points P ( 1 ) , P , ( 2 ) , P ...
Sivu 97
... Consider the three pattern hyperplanes ( lines ) in Fig . 6.3 for the case D = d + 1 = 2. The arrows attached to the lines point toward the posi- tive sides of the lines . These lines divide the weight space into six regions ; thus ...
... Consider the three pattern hyperplanes ( lines ) in Fig . 6.3 for the case D = d + 1 = 2. The arrows attached to the lines point toward the posi- tive sides of the lines . These lines divide the weight space into six regions ; thus ...
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 |