Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 12
Sivu 67
... pattern hyperplanes pass through the origin of weight space . WD = Corresponding to the training subsets X1 and X2 there are subsets of D - dimensional , augmented patterns Y1 and Y2 . Each element of y1 and Y2 is obtained by augmenting ...
... pattern hyperplanes pass through the origin of weight space . WD = Corresponding to the training subsets X1 and X2 there are subsets of D - dimensional , augmented patterns Y1 and Y2 . Each element of y1 and Y2 is obtained by augmenting ...
Sivu 69
... pattern hyperplane . The most direct path to the other side is along a line normal to the pattern hyperplane . Such a motion can be achieved by adding the augmented pattern vector Y to W to create a new weight vector W ' . Each TLU ...
... pattern hyperplane . The most direct path to the other side is along a line normal to the pattern hyperplane . Such a motion can be achieved by adding the augmented pattern vector Y to W to create a new weight vector W ' . Each TLU ...
Sivu 75
... augmented pattern vector ; that is , gi ( X ) == W ( i ) . Y for i = 1 , R ( 4.7 ) Simple extensions of the training procedures already discussed can be used to train a general linear machine . Suppose we have a set y of augmented ...
... augmented pattern vector ; that is , gi ( X ) == W ( i ) . Y for i = 1 , R ( 4.7 ) Simple extensions of the training procedures already discussed can be used to train a general linear machine . Suppose we have a set y of augmented ...
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 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 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 |