Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 31
Sivu 52
... equal probability density ( z12 - 20122122 + 222 = constant ) are ellipses , cen- tered on the origin , whose major axes lie along the line z1 = 22. The eccentricities of the ellipses are equal to 2012 VI + 012 When σ12 is zero , the ...
... equal probability density ( z12 - 20122122 + 222 = constant ) are ellipses , cen- tered on the origin , whose major axes lie along the line z1 = 22. The eccentricities of the ellipses are equal to 2012 VI + 012 When σ12 is zero , the ...
Sivu 58
... equal to rank Q.Q , which is equal to rank Q1 , and since rank Q ; ≤ min ( d , N1 ) , rank ( Σ ) ; < d if N ; < d . If N ; ≥ d , Q. will have rank equal to d if and only if there are no linear dependencies among the rows of Q. Or ...
... equal to rank Q.Q , which is equal to rank Q1 , and since rank Q ; ≤ min ( d , N1 ) , rank ( Σ ) ; < d if N ; < d . If N ; ≥ d , Q. will have rank equal to d if and only if there are no linear dependencies among the rows of Q. Or ...
Sivu 90
... equal to -Ŷ , and whose other components are all equal to zero . We apply this rule to each element of Sy to generate the sequence Sz . The final step of the proof is to form a sequence Sy of RD - dimensional weight vectors from the ...
... equal to -Ŷ , and whose other components are all equal to zero . We apply this rule to each element of Sy to generate the sequence Sz . The final step of the proof is to form a sequence Sy of RD - dimensional weight vectors from the ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
LAYERED MACHINES | 95 |
Tekijänoikeudet | |
1 muita osia ei näytetty
Muita painoksia - Näytä kaikki
Yleiset termit ja lausekkeet
assume belonging to category Chapter cluster committee machine committee TLUS components correction increment covariance matrix decision surfaces denote diagonal matrix dot products error-correction procedure Euclidean distance example Fix and Hodges function g(X g₁(X 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 vector 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 |