Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 12
Sivu 7
... Transformation properties of layered machines , 103 6.6 A sufficient condition for image - space linear separability , 107 6.7 Derivation of a discriminant function for a layered machine , 109 6.8 Bibliographical and historical remarks ...
... Transformation properties of layered machines , 103 6.6 A sufficient condition for image - space linear separability , 107 6.7 Derivation of a discriminant function for a layered machine , 109 6.8 Bibliographical and historical remarks ...
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 12 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 12 space depends on the values of the weights in the second layer . For a given set of weights ...
Sivu 105
... transformation numbers 1 , 2 , and 3 , we have an easy means of determining the trans- formation from the pattern ... transformed into the single point ( 1 , −1 , −1 ) in image space . The numbers associated with the image points in ...
... transformation numbers 1 , 2 , and 3 , we have an easy means of determining the trans- formation from the pattern ... transformed into the single point ( 1 , −1 , −1 ) in image space . The numbers associated with the image points in ...
Sisältö
Preface vii | 11 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented important initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern hyperplane pattern space pattern vector pattern-classifying piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space Stanford step subsidiary discriminant Suppose terns theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |