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 where given by 12 , 1 p ( 21,22 ) = 27 √1-1 exp ( - 2 121220122122 + 222 ) 1 — σ122 ( 3.18 ) 012 VI 2 which is called the ...
... density function In terms of these normalized variables the bivariate normal density function is expressed by where given by 12 , 1 p ( 21,22 ) = 27 √1-1 exp ( - 2 121220122122 + 222 ) 1 — σ122 ( 3.18 ) 012 VI 2 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 |
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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 step subsidiary discriminant Suppose terns 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 |