Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 11
Sivu 100
... dot products with Y. Let the weight vec- tors at this stage be given by W1 ( * ) , W2 ( * ) , and Wp ( ) . · In ... dot product with Y minus the number having a negative dot product with Y. * Thus , N is the sum operated on by the ...
... dot products with Y. Let the weight vec- tors at this stage be given by W1 ( * ) , W2 ( * ) , and Wp ( ) . · In ... dot product with Y minus the number having a negative dot product with Y. * Thus , N is the sum operated on by the ...
Sivu 101
... dot products closest to zero . ( Ties are resolved arbitrarily . ) These , in one sense , are the easiest to adjust . The adjustment is achieved by the familiar process of adding ( or subtracting ) the pattern vector to ( or from ) the ...
... dot products closest to zero . ( Ties are resolved arbitrarily . ) These , in one sense , are the easiest to adjust . The adjustment is achieved by the familiar process of adding ( or subtracting ) the pattern vector to ( or from ) the ...
Sivu 103
... dot products with Y1 ) . At the next stage , examining the weight - vector positions with respect to the Y2 pattern hyperplane we see that all of them ( hence , again , the majority ) are on the correct side ; thus , no adjustments are ...
... dot products with Y1 ) . At the next stage , examining the weight - vector positions with respect to the Y2 pattern hyperplane we see that all of them ( hence , again , the majority ) are on the correct side ; thus , no adjustments are ...
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 step subsidiary discriminant Suppose terns 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 |