Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 10
Sivu 33
... derivations to follow in this and subsequent sections , we shall use some facts from geometry which , while obvious for two- and three - dimensional spaces , happen to be valid in any finite - dimensional space . Of course , each of ...
... derivations to follow in this and subsequent sections , we shall use some facts from geometry which , while obvious for two- and three - dimensional spaces , happen to be valid in any finite - dimensional space . Of course , each of ...
Sivu 62
... derivation is given by Minsky . Winder1 has determined that the weights specified by Eqs . ( 3 ∙ 14 ) and ( 3 ∙ 15 ) of this example will realize only a small percentage of the linearly separable switching functions and suggests ...
... derivation is given by Minsky . Winder1 has determined that the weights specified by Eqs . ( 3 ∙ 14 ) and ( 3 ∙ 15 ) of this example will realize only a small percentage of the linearly separable switching functions and suggests ...
Sivu 77
... derivation of L ( N , d ) given in the footnote on page 67 follows the derivation by Cameron , 12 The error - correction training procedures discussed in Sec . 4.3 stem . from a variety of sources . The fixed - increment and absolute ...
... derivation of L ( N , d ) given in the footnote on page 67 follows the derivation by Cameron , 12 The error - correction training procedures discussed in Sec . 4.3 stem . from a variety of sources . The fixed - increment and absolute ...
Sisältö
Preface vii | 11 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
5 muita osia ei näytetty
Muita painoksia - Näytä kaikki
Yleiset termit ja lausekkeet
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 |