Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 23
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 x and x2 is more complicated * than that of Eq ...
... zero , the contours of equal probability are circles ( zero eccentricity ) . The expression for the bivariate normal density function for the unnormalized and untranslated variables x and x2 is more complicated * than that of Eq ...
Sivu 70
... 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 . That is , we desire that W.Y = ( WcY ) Y > 0 • W'.Y = ( WcY ) · Y < 0 • for W Y erroneously nonpositive and • for ...
... 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 . That is , we desire that W.Y = ( WcY ) Y > 0 • W'.Y = ( WcY ) · Y < 0 • for W Y erroneously nonpositive and • for ...
Sivu 100
... zero components . The patterns are arranged in a train- ing sequence and presented to the machine , one at a time ... zero and the machine response will be +1 . Since P is odd , NÂ can never equal zero or be even . We have assumed that ...
... zero components . The patterns are arranged in a train- ing sequence and presented to the machine , one at a time ... zero and the machine response will be +1 . Since P is odd , NÂ can never equal zero or be even . We have assumed that ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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assume belonging to category cluster committee machine committee TLUS components correction increment covariance matrix decision surfaces denote diagonal matrix dot products error-correction procedure Euclidean distance example Fix and Hodges function g(X g₁(X gi(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 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 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 |