Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 14
Sivu 89
... reduced training sequence Sŷ and the reduced weight - vector sequences Sŵ1 , SWR a corresponding sequence of vectors from the set Z. Let us denote this sequence of vectors from Z by the symbol Sz . Corresponding to the kth member , Ŷ of ...
... reduced training sequence Sŷ and the reduced weight - vector sequences Sŵ1 , SWR a corresponding sequence of vectors from the set Z. Let us denote this sequence of vectors from Z by the symbol Sz . Corresponding to the kth member , Ŷ of ...
Sivu 90
... reduced weight - vector sequences , Sŵ ,, . . . , SŴR . Let V be the kth member of the sequence Sv . If the respective kth mem- bers of Sŵ ,, . .. , Sŵ are given by W , ( * ) , ŴR ( * ) then V is deter- mined as follows : The first ...
... reduced weight - vector sequences , Sŵ ,, . . . , SŴR . Let V be the kth member of the sequence Sv . If the respective kth mem- bers of Sŵ ,, . .. , Sŵ are given by W , ( * ) , ŴR ( * ) then V is deter- mined as follows : The first ...
Sivu 136
... training theorem , 87 rth - order polynomial functions , 30 , 38 Randall , 62 , 63 Rank of sample covariance matrix , 58 Rao , 12 , 13 Reduced training sequence , 82 Reduced weight - vector sequence , 82 Reduction of number 136 INDEX 65.
... training theorem , 87 rth - order polynomial functions , 30 , 38 Randall , 62 , 63 Rank of sample covariance matrix , 58 Rao , 12 , 13 Reduced training sequence , 82 Reduced weight - vector sequence , 82 Reduction of number 136 INDEX 65.
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 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 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 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 |