Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 22
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 67
... point representing the weight values w1 = 0 , w2 ... , WD = 0 satisfies Eq . ( 4.2 ) regard- less of Y. Therefore all pattern hyperplanes pass through the origin of weight space . = Corresponding to the training subsets X1 and X2 there ...
... point representing the weight values w1 = 0 , w2 ... , WD = 0 satisfies Eq . ( 4.2 ) regard- less of Y. Therefore all pattern hyperplanes pass through the origin of weight space . = Corresponding to the training subsets X1 and X2 there ...
Sivu 71
... weight point 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 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 |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern hyperplane pattern space pattern vector pattern-classifying piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space step subsidiary discriminant Suppose terns 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 |