Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 22
Sivu 52
... zero , the contours of equal probability are circles ( zero eccentricity ) . The expression for the bivariate normal density function for the unnormalized and untranslated variables 1 and 2 is more complicated * than that of Eq . ( 3 ...
... zero , the contours of equal probability are circles ( zero eccentricity ) . The expression for the bivariate normal density function for the unnormalized and untranslated variables 1 and 2 is more complicated * than that of Eq . ( 3 ...
Sivu 70
... zero . When c is equal to one , for example , each weight is altered by the addition ( or subtraction ) of the ... zero . In one case , c is taken to be the smallest integer which will make the value of W Y cross the threshold of zero ...
... zero . When c is equal to one , for example , each weight is altered by the addition ( or subtraction ) of the ... zero . In one case , c is taken to be the smallest integer which will make the value of W Y cross the threshold of zero ...
Sivu 109
... zero vector is a vertex belonging to 1 ( ) ; otherwise is chosen to be any convenient positive number . In this way all the pattern vectors which map into the image - space zero vector are automatically classified correctly . Clearly ...
... zero vector is a vertex belonging to 1 ( ) ; otherwise is chosen to be any convenient positive number . In this way all the pattern vectors which map into the image - space zero vector are automatically classified correctly . Clearly ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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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 |