Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 48
Sivu 1
... example of a sorting task is weather prediction . A forecast must be based on certain weather measurements , for example , the present values of atmospheric pressure and atmospheric pressure changes at a number of stations . Suppose ...
... example of a sorting task is weather prediction . A forecast must be based on certain weather measurements , for example , the present values of atmospheric pressure and atmospheric pressure changes at a number of stations . Suppose ...
Sivu 5
... example , consider the sets shown in Fig . 1.2 where d = 2 and R = 3. A point in the plane is mapped into the numbers 1 , 2 , or 3 , according to its membership in R1 , R2 , or R3 , respectively . For example , the pattern ( 5 , -3 ) ...
... example , consider the sets shown in Fig . 1.2 where d = 2 and R = 3. A point in the plane is mapped into the numbers 1 , 2 , or 3 , according to its membership in R1 , R2 , or R3 , respectively . For example , the pattern ( 5 , -3 ) ...
Sivu 47
... example Suppose that we wish to design a machine to categorize patterns each consisting of d binary components . ( Each x ; = 1 or 0. ) Let us assume that R = 2 ; that is , there are two categories , labeled category 1 and category 2 ...
... example Suppose that we wish to design a machine to categorize patterns each consisting of d binary components . ( Each x ; = 1 or 0. ) Let us assume that R = 2 ; that is , there are two categories , labeled category 1 and category 2 ...
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 |