Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 24
Sivu 3
... discussed previously , we might have d and X1 = 1023 X2 X3 = = 1013 4 X4 - -7 = 4 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 and X1 = 1023 X2 X3 = = 1013 4 X4 - -7 = 4 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 , Y2 , . . . , YR which are linearly separable . The subset y ; con- Yi tains all training patterns ...
... discussed can be used to train a general linear machine . Suppose we have a set y of augmented training patterns divided into subsets Y1 , Y2 , . . . , YR which are linearly separable . The subset y ; con- Yi tains all training patterns ...
Sivu 125
... discussed in Sec . 7-3 , has been recognized by several workers . Duda and Singleton1 have studied and experimented with non- parametric training rules that appear to escape this disadvantage . The Fix and Hodges method , presented in ...
... discussed in Sec . 7-3 , has been recognized by several workers . Duda and Singleton1 have studied and experimented with non- parametric training rules that appear to escape this disadvantage . The Fix and Hodges method , presented in ...
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 |