Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 33
Sivu 37
... pattern space satisfying the equations g ( X ) = 0 are families of surfaces . The separations that these surfaces ( called surfaces ) effect on a set X of N points are called dichotomies . If there is no surface in the pattern space ...
... pattern space satisfying the equations g ( X ) = 0 are families of surfaces . The separations that these surfaces ( called surfaces ) effect on a set X of N points are called dichotomies . If there is no surface in the pattern space ...
Sivu 104
... pattern space is trans- formed into one of the vertices of a Pi - dimensional hypercube . This hypercube we shall call the first image space or the I1 space . The trans- formation between the pattern space and the Ii space depends on ...
... pattern space is trans- formed into one of the vertices of a Pi - dimensional hypercube . This hypercube we shall call the first image space or the I1 space . The trans- formation between the pattern space and the Ii space depends on ...
Sivu 105
... space 2 ( a ) Pattern space FIGURE 6.6 Pattern - space to image - space transformation numbers 1 , 2 , and 3 , we have an easy means of determining the trans- formation from the pattern space to the image space . For example , pat ...
... space 2 ( a ) Pattern space FIGURE 6.6 Pattern - space to image - space transformation numbers 1 , 2 , and 3 , we have an easy means of determining the trans- formation from the pattern space to the image space . For example , pat ...
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 |