Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 25
Sivu 3
... discussed previously , we might have d = 4 and X1 = 1023 X2 = 1013 X3 = X 4 = 4 - 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 X2 = 1013 X3 = X 4 = 4 - 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 75
... discussed can be used to train a general linear machine . Suppose we have a set y of augmented training patterns divided into subsets Y1 , 2 , . . . , YR which are linearly separable . The subset y ; con- tains all training patterns in ...
... discussed can be used to train a general linear machine . Suppose we have a set y of augmented training patterns divided into subsets Y1 , 2 , . . . , YR which are linearly separable . The subset y ; con- tains all training patterns in ...
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 Hoffs 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 Hoffs introduced a similar rule at substantially the same time ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
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adjusted apply assume bank called cells changes Chapter cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described dichotomies 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 classifier pattern hyperplane pattern space pattern vector 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 theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors X1 and X2 Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |