Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 49
... distributions . As an example , consider the case of N successive Bernoulli trials where the probability of success is some number p . We assume that the value of p is unknown , but that it is governed by a uniform probability distribution ...
... distributions . As an example , consider the case of N successive Bernoulli trials where the probability of success is some number p . We assume that the value of p is unknown , but that it is governed by a uniform probability distribution ...
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 X 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 X 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 ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
11 | 30 |
PARAMETRIC TRAINING METHODS | 43 |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components Computer 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 terns theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |