Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 16
Sivu 6
... regions which we shall call decision regions . The ith region R ; is the set of points which map into the ith cate- gory number . For convenience , we shall arbitrarily assume that patterns which lie on decision surfaces do not belong ...
... regions which we shall call decision regions . The ith region R ; is the set of points which map into the ith cate- gory number . For convenience , we shall arbitrarily assume that patterns which lie on decision surfaces do not belong ...
Sivu 19
... regions and surfaces resulting from linear discriminant functions S 13 R3 R2 2 R1 S 12 S 23 FIGURE 2.3 Decision regions for a minimum - distance classifier with respect to the points P1 , P2 , and P3 In many cases some of the ...
... regions and surfaces resulting from linear discriminant functions S 13 R3 R2 2 R1 S 12 S 23 FIGURE 2.3 Decision regions for a minimum - distance classifier with respect to the points P1 , P2 , and P3 In many cases some of the ...
Sivu 20
... region lies entirely within the region ) . It will be left as an exercise for the reader to verify that the decision . regions of a linear machine are always convex . 2.5 Linear classifications of patterns Suppose we have a finite set X ...
... region lies entirely within the region ) . It will be left as an exercise for the reader to verify that the decision . regions of a linear machine are always convex . 2.5 Linear classifications of patterns Suppose we have a finite set X ...
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 |