Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 50
... 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 discriminant 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 discriminant function ...
Sivu 51
... density function In terms of these normalized variables the bivariate normal density function is expressed by 1 121220122122 +227 p ( 21,22 ) = exp - ( 3.18 ) 2π V1 - 0122 2 1- 0122 where σ12 , given by which is called the covariance or ...
... density function In terms of these normalized variables the bivariate normal density function is expressed by 1 121220122122 +227 p ( 21,22 ) = exp - ( 3.18 ) 2π V1 - 0122 2 1- 0122 where σ12 , given by which is called the covariance or ...
Sivu 59
... density for X , given M , is still given by Eq . ( 3-38 ) . The unconditional density for X can now be obtained by inspection , since Z and M are independent . Then X will have mean u and covariance matrix Σ + K. That is , p ( X ) ~ N ...
... density for X , given M , is still given by Eq . ( 3-38 ) . The unconditional density for X can now be obtained by inspection , since Z and M are independent . Then X will have mean u and covariance matrix Σ + K. That is , p ( X ) ~ N ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding decision surfaces define denote density depends derivation described Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial layered machine linear dichotomies linear discriminant functions linear machine linearly separable measurements negative networks normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem proof properties proved PWL machine quadric reduced regions respect response rule sample mean selection separable shown side space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods training procedure training sequence training subsets transformation values weight vectors zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |