Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 49
Sivu 69
... pattern Y in y , a TLU with a weight vector W has a response which is either erroneous ( Y. W < 0 ) or undefined ( Y. W = 0 ) . That is , W is either on the negative side of or on the pattern hyperplane corresponding to Y. This error ...
... pattern Y in y , a TLU with a weight vector W has a response which is either erroneous ( Y. W < 0 ) or undefined ( Y. W = 0 ) . That is , W is either on the negative side of or on the pattern hyperplane corresponding to Y. This error ...
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 | |
3 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 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 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 |