Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 15
Sivu 67
... pattern hyperplanes pass through the origin of weight space . = 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 . = 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 |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern hyperplane pattern space pattern vector pattern-classifying piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space step subsidiary discriminant Suppose terns theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors X1 and X2 Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |