Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 21
Sivu 20
... region lies entirely within the region ) . It will be left as an exercise for the reader to verify that the decision regions of a linear machine are always convex . 2.5 Linear classifications of patterns , XN } , Suppose we have a ...
... region lies entirely within the region ) . It will be left as an exercise for the reader to verify that the decision regions of a linear machine are always convex . 2.5 Linear classifications of patterns , XN } , Suppose we have a ...
Sivu 67
... region in weight space corresponding to it . * For any given linear dichotomy , the corre- * If we count the number of regions in weight space formed by N augmented pattern hyperplanes , we obtain the number of dichotomies of N d ...
... region in weight space corresponding to it . * For any given linear dichotomy , the corre- * If we count the number of regions in weight space formed by N augmented pattern hyperplanes , we obtain the number of dichotomies of N d ...
Sivu 68
... regions on the Nth hyper- plane divides one of the original R ( N 1 , D ) regions in the D - dimensional space into two parts . Therefore , the addition of the Nth plane can add at most R ( N1 , D − 1 ) new regions . This fact gives ...
... regions on the Nth hyper- plane divides one of the original R ( N 1 , D ) regions in the D - dimensional space into two parts . Therefore , the addition of the Nth plane can add at most R ( N1 , D − 1 ) new regions . This fact gives ...
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
assume augmented pattern belonging to category Chapter cluster committee machine committee TLUS components 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 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 shown in Fig solution weight vectors subsets X1 subsidiary discriminant functions Suppose terns 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 |