Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 20
Sivu 53
... distribution . The notation used in Eq . ( 3 · 20 ) to describe the normal distribution can be made more compact if we define and use the following matrices . Let the pattern vector X be a column vector ( a 2 × 1 matrix ) with compo ...
... distribution . The notation used in Eq . ( 3 · 20 ) to describe the normal distribution can be made more compact if we define and use the following matrices . Let the pattern vector X be a column vector ( a 2 × 1 matrix ) with compo ...
Sivu 54
... distribution which describes the joint probability density of d components . Patterns selected according to this joint proba- bility distribution will be called multivariate normal patterns or , more simply , normal patterns . The ...
... distribution which describes the joint probability density of d components . Patterns selected according to this joint proba- bility distribution will be called multivariate normal patterns or , more simply , normal patterns . The ...
Sivu 123
... distribution of which the points are samples . It is true that if the probability distribution has only one mode ( uni- modal ) , then the center of gravity of a set of points is often a good esti- mate for this mode . In multimodal ...
... distribution of which the points are samples . It is true that if the probability distribution has only one mode ( uni- modal ) , then the center of gravity of a set of points is often a good esti- mate for this mode . In multimodal ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |