Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 21
Sivu 3
... discussed previously , we might have d = 4 and X1 = 1023 X2 = 1013 X3 = 4 X 4 = -7 These four numbers might be the current atmospheric pressures ( in millibars ) at stations 1 and 2 and the pressure changes at these stations ...
... discussed previously , we might have d = 4 and X1 = 1023 X2 = 1013 X3 = 4 X 4 = -7 These four numbers might be the current atmospheric pressures ( in millibars ) at stations 1 and 2 and the pressure changes at these stations ...
Sivu 75
... discussed can be used to train a general linear machine . Suppose we have a set y of augmented training patterns divided into subsets Y1 , 2 , . . . , YR which are linearly separable . The subset y ; con- Y ; tains all training patterns ...
... discussed can be used to train a general linear machine . Suppose we have a set y of augmented training patterns divided into subsets Y1 , 2 , . . . , YR which are linearly separable . The subset y ; con- Y ; tains all training patterns ...
Sivu 118
... discussed several nonparametric training methods . Generally , nonparametric training methods are to be preferred to parametric ones because no assumptions need be made about the forms of underlying probability distributions . This ...
... discussed several nonparametric training methods . Generally , nonparametric training methods are to be preferred to parametric ones because no assumptions need be made about the forms of underlying probability distributions . This ...
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 |