Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 13
Sivu 3
... changes at these stations , respectively . Suppose that there are R categories into which the patterns must be sorted . We shall label these categories by the integers 1 , 2 , . . . , R. One of these integers , perhaps R , might ...
... changes at these stations , respectively . Suppose that there are R categories into which the patterns must be sorted . We shall label these categories by the integers 1 , 2 , . . . , R. One of these integers , perhaps R , might ...
Sivu 8
... changes in the organization , structure , or parameter values of the parts of the machine , or it can occur before hardware construction by making these changes on a simulated machine using , for example , a digital computer . We shall ...
... changes in the organization , structure , or parameter values of the parts of the machine , or it can occur before hardware construction by making these changes on a simulated machine using , for example , a digital computer . We shall ...
Sivu 59
... changes as a result of being given a set of training patterns . We can see how it changes by first calculating an a posteriori density function for the mean vector M. ( By an a posteriori density function we mean the density function ...
... changes as a result of being given a set of training patterns . We can see how it changes by first calculating an a posteriori density function for the mean vector M. ( By an a posteriori density function we mean the density function ...
Sisältö
Preface vii | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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assume 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 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 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 |