Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 40
Sivu 89
... suppose that Y1 , Y2 , ... , YR are linearly separable with a set of solution weight vectors W1 , W2 , ... , WR ; then observe that Z is linearly contained with an RD- dimensional vector V ( W1 , W2 , . . . , WR ) . Conversely , suppose ...
... suppose that Y1 , Y2 , ... , YR are linearly separable with a set of solution weight vectors W1 , W2 , ... , WR ; then observe that Z is linearly contained with an RD- dimensional vector V ( W1 , W2 , . . . , WR ) . Conversely , suppose ...
Sivu 92
... Suppose it is not , but instead lies outside * at a distance A from one of the pattern hyperplanes bounding W. But the pattern corresponding to this hyperplane will eventually occur in Sy , say at the kth step . Suppose k to be ...
... Suppose it is not , but instead lies outside * at a distance A from one of the pattern hyperplanes bounding W. But the pattern corresponding to this hyperplane will eventually occur in Sy , say at the kth step . Suppose k to be ...
Sivu 123
... Suppose that patterns are presented to the PWL machine one at a time from a training sequence . Let the initial weight vectors be selected arbitrarily . * We shall describe the adjustments to be made at the kth step . Suppose that the ...
... Suppose that patterns are presented to the PWL machine one at a time from a training sequence . Let the initial weight vectors be selected arbitrarily . * We shall describe the adjustments to be made at the kth step . Suppose that the ...
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,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |