Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Since each of these discriminant functions gi ( 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 gi ( 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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2.7 , 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 ...
2.7 , 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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... 72 , 75 disadvantages of , 118 graphical example of , 71 for linear machines , 75 numerical example of , 72 for $ machines , 76 for piecewise linear machines , 116 for TLUS , 69 Estimation of modes , 123 Euclidean distance , 16 from ...
... 72 , 75 disadvantages of , 118 graphical example of , 71 for linear machines , 75 numerical example of , 72 for $ machines , 76 for piecewise linear machines , 116 for TLUS , 69 Estimation of modes , 123 Euclidean distance , 16 from ...
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adjusted apply assume bank belonging to category called changes Chapter classifier cluster committee components consider consists contains correction corresponding decision surfaces define denote density depends derivation described discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed gi(X given illustrated implemented important initial known layered machine linear dichotomies linear machine linearly separable negative 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 selected separable shown side space specific Stanford step Suppose theorem theory threshold training methods training patterns 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 |