Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 25
Sivu 28
... zero only for X = When these conditions are met , both 0 the matrix A and the quadratic form are called positive definite . If A has one or more of its eigenvalues equal to zero and all the others positive , then the quadratic form will ...
... zero only for X = When these conditions are met , both 0 the matrix A and the quadratic form are called positive definite . If A has one or more of its eigenvalues equal to zero and all the others positive , then the quadratic form will ...
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 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 .た Since P is odd , N can never equal zero or be even . k We have assumed ...
... 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 .た Since P is odd , N can never equal zero or be even . k We have assumed ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern hyperplane pattern space pattern vector pattern-classifying piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors X1 and X2 Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |