Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 31
Sivu 9
... parameters were known , ade- quate discriminant functions based on them could be directly specified . In the parametric training methods the training set is used for the purpose of obtaining estimates of the parameter values , and the ...
... parameters were known , ade- quate discriminant functions based on them could be directly specified . In the parametric training methods the training set is used for the purpose of obtaining estimates of the parameter values , and the ...
Sivu 44
... parameter set : the number p ( i ) and the parameters of the function p ( Xi ) . The parametric training method for the design of discriminant functions then consists of three steps : 1. The discriminant functions are expressed in terms ...
... parameter set : the number p ( i ) and the parameters of the function p ( Xi ) . The parametric training method for the design of discriminant functions then consists of three steps : 1. The discriminant functions are expressed in terms ...
Sivu 58
... assume certain probability distributions for M ; and Σ ; it is meaning- less to speak of optimum estimates . derived from the training set as if they were the 58 PARAMETRIC TRAINING METHODS Learning the mean vector of normal patterns,
... assume certain probability distributions for M ; and Σ ; it is meaning- less to speak of optimum estimates . derived from the training set as if they were the 58 PARAMETRIC TRAINING METHODS Learning the mean vector of normal patterns,
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 |