Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 22
Sivu 3
... discussed previously , we might have d = 4 and X1 = 1023 X2 = 1013 = 4 -7 X3 X4 = 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 = 4 -7 X3 X4 = 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 |
Tekijänoikeudet | |
3 muita osia ei näytetty
Muita painoksia - Näytä kaikki
Yleiset termit ja lausekkeet
assume augmented pattern belonging to category Chapter cluster committee machine committee TLUS correction increment covariance matrix d-dimensional decision surfaces denote diagonal matrix discussed dot products error-correction procedure Euclidean distance example Fix and Hodges function g(X g₁(X given Hodges method hypersphere image-space implemented initial weight vectors ith bank layer of TLUS layered machine linear dichotomies linear discriminant functions linearly separable loss function mean vector minimum-distance classifier mode-seeking networks nonparametric number of patterns p₁ parameters partition pattern classifier pattern hyperplane pattern space pattern vector patterns belonging perceptron piecewise linear plane point sets positive probability distributions prototype pattern PWL machine quadratic form quadric function rule sample covariance matrix shown in Fig solution weight vectors Stanford subsets X1 subsidiary discriminant functions Suppose terns TLU response training patterns training sequence training set training subsets transformation two-layer machine values W₁ weight point weight space weight-vector sequence X1 and X2 zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |