Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 17
Sivu 66
... Weight space Before discussing training methods for a TLU it will be helpful to formu- late a geometric representation in which the TLU weight values are the coordinates of a point in a multidimensional space . This space , which we ...
... Weight space Before discussing training methods for a TLU it will be helpful to formu- late a geometric representation in which the TLU weight values are the coordinates of a point in a multidimensional space . This space , which we ...
Sivu 70
... weight vector W responds incorrectly to an augmented pattern vector Y. The weight vec- tor is then changed to a new ... point W and the pattern hyperplane corresponding to Y. The quantity WY W ' Y is then proportional to the distance between ...
... weight vector W responds incorrectly to an augmented pattern vector Y. The weight vec- tor is then changed to a new ... point W and the pattern hyperplane corresponding to Y. The quantity WY W ' Y is then proportional to the distance between ...
Sivu 71
... weight point 2 3 Final weight point FIGURE 4.2 A graphical illustration of error - correction training X guarantee that the pattern hyperplane is crossed and the response cor- rected . In the third case c is so chosen that the distance ...
... weight point 2 3 Final weight point FIGURE 4.2 A graphical illustration of error - correction training X guarantee that the pattern hyperplane is crossed and the response cor- rected . In the third case c is so chosen that the distance ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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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 |