Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 21
Sivu 3
... discussed previously , we might have d = 4 and X1 = 1023 1013 X2 = = X3 4 X4 = -7 These four numbers might be the current atmospheric pressures ( in millibars ) at stations 1 and 2 and the pressure changes at these stations ...
... discussed previously , we might have d = 4 and X1 = 1023 1013 X2 = = X3 4 X4 = -7 These four numbers might be the current atmospheric pressures ( in millibars ) at stations 1 and 2 and the pressure changes at these stations ...
Sivu 77
... discussed in Sec . 4.3 stem . from a variety of sources . The fixed - increment and absolute correction rules were first proposed by Rosenblatt , 13 although Widrow and Hoff introduced a similar rule at substantially the same time ...
... discussed in Sec . 4.3 stem . from a variety of sources . The fixed - increment and absolute correction rules were first proposed by Rosenblatt , 13 although Widrow and Hoff introduced a similar rule at substantially the same time ...
Sivu 118
... discussed several nonparametric training methods . Generally , nonparametric training methods are to be preferred to parametric ones because no assumptions need be made about the forms of underlying probability distributions . This ...
... discussed several nonparametric training methods . Generally , nonparametric training methods are to be preferred to parametric ones because no assumptions need be made about the forms of underlying probability distributions . This ...
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 |