Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 16
Sivu 4
... problem of what to measure In assuming that the data to be classified consist of d real numbers , we are obliged to mention , at least briefly , the difficulties that attend selecting these numbers from any given physical situation ...
... problem of what to measure In assuming that the data to be classified consist of d real numbers , we are obliged to mention , at least briefly , the difficulties that attend selecting these numbers from any given physical situation ...
Sivu 12
... problems . 6 8 9 The problem of selection of measurements has also received some attention by both statisticians and engineers . Bahadur , Lewis , and Marill and Green 10 propose and discuss tests for the " effectiveness " of ...
... problems . 6 8 9 The problem of selection of measurements has also received some attention by both statisticians and engineers . Bahadur , Lewis , and Marill and Green 10 propose and discuss tests for the " effectiveness " of ...
Sivu 104
... problem for layered machines can then be viewed as a problem of adjusting the various layers of weights such that the transformations implemented by the first N 1 layers result in linearly separable subsets at the ( N - 1 ) th layer ...
... problem for layered machines can then be viewed as a problem of adjusting the various layers of weights such that the transformations implemented by the first N 1 layers result in linearly separable subsets at the ( N - 1 ) th layer ...
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 decision surfaces denote diagonal matrix discussed dot products error-correction procedure Euclidean distance example Fix and Hodges g₁(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 second layer shown in Fig solution weight vectors Stanford subsets X1 subsidiary discriminant functions Suppose terns training patterns training sequence training set training subsets transformation two-layer machine values W₁ wa+1 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 |