Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Since each of these discriminant functions g : ( X ) is a piecewise linear function of the components of X we shall call them piecewise linear discriminant functions . * Any machine employing piecewise linear discriminant functions will ...
Since each of these discriminant functions g : ( X ) is a piecewise linear function of the components of X we shall call them piecewise linear discriminant functions . * Any machine employing piecewise linear discriminant functions will ...
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27 , a layered machine is a piecewise linear machine . A layered machine with P TLUS in the first layer has a total of 2P linear subsidiary discriminant functions ; these are divided into two classes ( corresponding to category 1 and ...
27 , a layered machine is a piecewise linear machine . A layered machine with P TLUS in the first layer has a total of 2P linear subsidiary discriminant functions ; these are divided into two classes ( corresponding to category 1 and ...
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Decision regions , 6 convexity of , 20 of a linear machine , 19 , 20 of a piecewise linear machine , 26 , 27 Decision surfaces , 5 , 18 , 19 equation of , 6 , 7 , 18 Decision theory , 44 Degrees of freedom , number of , for a ...
Decision regions , 6 convexity of , 20 of a linear machine , 19 , 20 of a piecewise linear machine , 26 , 27 Decision surfaces , 5 , 18 , 19 equation of , 6 , 7 , 18 Decision theory , 44 Degrees of freedom , number of , for a ...
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Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
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adjusted apply assume bank called cells changes Chapter cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described dichotomies discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented important initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector 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 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 |