Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 17
Sivu 10
... estimates for X1 and X2 might then be the respective sample means ( centers of gravity ) of the patterns in each ... estimated , Eq . ( 1 · 2 ) could be used for the specifi- cation of g ( X ) , and the parametric training process would ...
... estimates for X1 and X2 might then be the respective sample means ( centers of gravity ) of the patterns in each ... estimated , Eq . ( 1 · 2 ) could be used for the specifi- cation of g ( X ) , and the parametric training process would ...
Sivu 49
... estimates for the unknown probabili- + 1 * The reader with background in statistics will recall that there are circumstances in which it is possible to make optimum estimates of unknown probability values . These optimum estimates are ...
... estimates for the unknown probabili- + 1 * The reader with background in statistics will recall that there are circumstances in which it is possible to make optimum estimates of unknown probability values . These optimum estimates are ...
Sivu 58
... estimates of M ; and ; , respectively . The use of these estimates to specify the discriminant functions would constitute a para- metric training method . i i An expression that is somewhat simpler than the one in Eq . ( 3 · 36 ) can be ...
... estimates of M ; and ; , respectively . The use of these estimates to specify the discriminant functions would constitute a para- metric training method . i i An expression that is somewhat simpler than the one in Eq . ( 3 · 36 ) can be ...
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 |