Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 26
Sivu 26
... piecewise linear function of the components of X we shall call them piecewise linear discriminant functions . * Any machine employing piecewise linear discriminant functions will be called a piecewise linear machine , of which a minimum ...
... piecewise linear function of the components of X we shall call them piecewise linear discriminant functions . * Any machine employing piecewise linear discriminant functions will be called a piecewise linear machine , of which a minimum ...
Sivu 112
... linear combi- nation of the functions f1 , fp . Since each of the f1 , fp is a linear function of the pattern vector ... piecewise linear machine . • 9 • 9 A layered machine with P TLUS in the first layer has a total of 2o linear ...
... linear combi- nation of the functions f1 , fp . Since each of the f1 , fp is a linear function of the pattern vector ... piecewise linear machine . • 9 • 9 A layered machine with P TLUS in the first layer has a total of 2o linear ...
Sivu 134
... linear machine , 19 , 20 of a piecewise linear machine , 26 , 27 Decision surfaces , 5 , 18 , 19 equation of , 6 , 7 , 18 Decision theory , 44 Degrees of freedom , number of , for a hypersphere , 38 for functions , 30 for a quadric ...
... linear machine , 19 , 20 of a piecewise linear machine , 26 , 27 Decision surfaces , 5 , 18 , 19 equation of , 6 , 7 , 18 Decision theory , 44 Degrees of freedom , number of , for a hypersphere , 38 for functions , 30 for a quadric ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
6 muita osia ei näytetty
Muita painoksia - Näytä kaikki
Yleiset termit ja lausekkeet
assume augmented pattern belonging to category Chapter cluster committee machine committee TLUS components correction increment covariance matrix decision surfaces denote diagonal matrix discussed dot products error-correction procedure Euclidean distance example Fix and Hodges 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 parametric training partition pattern hyperplane pattern points pattern space pattern vector pattern-classifying patterns belonging perceptron piecewise linear plane point sets positive probability distributions prototype pattern PWL machine quadratic form quadric function rule sample covariance matrix second layer shown in Fig solution weight vectors Stanford subsets X1 subsidiary discriminant functions Suppose terns training patterns training sequence training set training subsets transformation two-layer machine values W₁ wa+1 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 |