Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 11
Sivu 46
... shown that an optimum classifying machine could be achieved by computing and comparing the lx ( i ) . The computations are particularly simple if the loss function ( ij ) is assumed to be of the type - λ ( ij ) = 1 − dij ( 3.5 ) where ...
... shown that an optimum classifying machine could be achieved by computing and comparing the lx ( i ) . The computations are particularly simple if the loss function ( ij ) is assumed to be of the type - λ ( ij ) = 1 − dij ( 3.5 ) where ...
Sivu 104
... shown for purposes of identification . The planes shown in Fig . 6.6a are the surfaces implemented by three TLUs . The arrows attached to each of 104 LAYERED MACHINES.
... shown for purposes of identification . The planes shown in Fig . 6.6a are the surfaces implemented by three TLUs . The arrows attached to each of 104 LAYERED MACHINES.
Sivu 106
... shown in Fig . 6 · 7b . The symbols☐ and O are again used to denote the category associated with each image - point vertex . Note that in this example the two subsets space vertices are linearly separable ; the plane shown is one which ...
... shown in Fig . 6 · 7b . The symbols☐ and O are again used to denote the category associated with each image - point vertex . Note that in this example the two subsets space vertices are linearly separable ; the plane shown is one which ...
Sisältö
Preface vii | 11 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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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 |