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 |
Tekijänoikeudet | |
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assume augmented pattern belonging to category Chapter cluster committee machine committee TLUS components correction increment covariance matrix d-dimensional decision surfaces denote diagonal matrix discussed dot products error-correction procedure Euclidean distance example Fix and Hodges gi(X given Hodges method hypersphere image-space implemented initial weight vectors ith bank layer of TLUS layered machine linear dichotomies linear discriminant functions linearly separable loss function mean vector minimum-distance classifier mode-seeking networks nonparametric number of patterns p₁ parameters parametric training partition pattern hyperplane pattern points pattern space pattern vector pattern-classifying patterns belonging perceptron piecewise linear plane point sets positive probability distributions prototype pattern PWL machine quadratic form quadric function rule sample covariance matrix shown in Fig solution weight vectors Stanford subsets X1 subsidiary discriminant functions Suppose terns TLU response training patterns training sequence training set training subsets transformation two-layer machine values W₁ weight point weight space weight-vector sequence X1 and X2 zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |