Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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... density function In the example of Sec . 3.5 , we assumed that the pattern components were statistically independent , binary , random variables . Such an assumption permitted a straightforward calculation of the ... density function,
... density function In the example of Sec . 3.5 , we assumed that the pattern components were statistically independent , binary , random variables . Such an assumption permitted a straightforward calculation of the ... density function,
Sivu 51
... density function In terms of these normalized variables the bivariate normal density function is expressed by p ( 21,23 ) = 2 = √1-01 , exp ( - 2π V1 0122 2 121220122122 + 2221 1 - σ122 ( 3.18 ) where σ12 , which is called the ...
... density function In terms of these normalized variables the bivariate normal density function is expressed by p ( 21,23 ) = 2 = √1-01 , exp ( - 2π V1 0122 2 121220122122 + 2221 1 - σ122 ( 3.18 ) where σ12 , which is called the ...
Sivu 59
... density function for X. This task is made simpler by observing that X can be regarded as the sum of M and another independent normal vector Z ; that is , X = Z + M ( 3.40 ) The vector Z has zero mean and covariance matrix Σ . The ...
... density function for X. This task is made simpler by observing that X can be regarded as the sum of M and another independent normal vector Z ; that is , X = Z + M ( 3.40 ) The vector Z has zero mean and covariance matrix Σ . The ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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 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 second layer 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 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 |