Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 46
Sivu 69
... pattern hyperplane . The most direct path to the other side is along a line normal to the pattern hyperplane . Such a motion can be achieved by adding the augmented pattern vector Y to W to create a new weight vector W ' . Each TLU ...
... pattern hyperplane . The most direct path to the other side is along a line normal to the pattern hyperplane . Such a motion can be achieved by adding the augmented pattern vector Y to W to create a new weight vector W ' . Each TLU ...
Sivu 75
... vector with an augmented pattern vector ; that is , gi ( X ) = W ( i ) . Y for i = 1 , R ( 4.7 ) Simple extensions of the training procedures already discussed can be used to train a general linear machine . Suppose we have a set y of ...
... vector with an augmented pattern vector ; that is , gi ( X ) = W ( i ) . Y for i = 1 , R ( 4.7 ) Simple extensions of the training procedures already discussed can be used to train a general linear machine . Suppose we have a set y of ...
Sivu 103
... vector positions with respect to the Y2 pattern hyperplane we see that all of them ( hence , again , the majority ) ... vector . If there are P1TLUS in the first layer , these TLUs transform the d - dimen- sional input pattern vector into ...
... vector positions with respect to the Y2 pattern hyperplane we see that all of them ( hence , again , the majority ) ... vector . If there are P1TLUS in the first layer , these TLUs transform the d - dimen- sional input pattern vector into ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding decision surfaces define denote density depends derivation described Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial layered machine linear dichotomies linear discriminant functions linear machine linearly separable measurements negative networks normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem proof properties proved PWL machine quadric reduced regions respect response rule sample mean selection separable shown side space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods training procedure training sequence training subsets transformation values weight vectors zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |