Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 37
... Corresponding to each point X in the pattern space there is a point F = { ƒ1 ( X ) , . ƒм ( X ) } in space ; therefore , corresponding to the set X of N points in Ž general position in the pattern space , there is a set F of N points in ...
... Corresponding to each point X in the pattern space there is a point F = { ƒ1 ( X ) , . ƒм ( X ) } in space ; therefore , corresponding to the set X of N points in Ž general position in the pattern space , there is a set F of N points in ...
Sivu 70
... corresponding pattern component . This adjustment may or may not actually correct the error for the pattern , depending on the value of W Y in relation to c . Variations of the above procedure make c dependent on the quantity WY , the ...
... corresponding pattern component . This adjustment may or may not actually correct the error for the pattern , depending on the value of W Y in relation to c . Variations of the above procedure make c dependent on the quantity WY , the ...
Sivu 89
... corresponding sequence of vectors from the set Z. Let us denote this sequence of vectors from Z by the symbol Sz . Corresponding to the kth member , of Sy is a vector Z in Sz . We determine Z as follows : Sup- pose that belongs to y ...
... corresponding sequence of vectors from the set Z. Let us denote this sequence of vectors from Z by the symbol Sz . Corresponding to the kth member , of Sy is a vector Z in Sz . We determine Z as follows : Sup- pose that belongs to y ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding decision surfaces define denote density depends derivation described Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial layered machine linear dichotomies linear discriminant functions linear machine linearly separable measurements negative networks 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 selection separable shown side space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods training procedure training sequence training subsets transformation values weight vectors zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,Lįszló Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |