Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 8
Sivu 115
... banks of sub- sidiary discriminators , each bank corresponding to one of the pattern categories . The machine classifies patterns as follows : A pattern X is presented to the machine and the values of all of the subsidiary linear ...
... banks of sub- sidiary discriminators , each bank corresponding to one of the pattern categories . The machine classifies patterns as follows : A pattern X is presented to the machine and the values of all of the subsidiary linear ...
Sivu 122
... banks of weight vectors , and the ith bank has L ; members . Any training method which adjusts the weight vectors in each bank so that each weight vector finally resides in the center of a cluster of like - category patterns will be ...
... banks of weight vectors , and the ith bank has L ; members . Any training method which adjusts the weight vectors in each bank so that each weight vector finally resides in the center of a cluster of like - category patterns will be ...
Sivu 124
... bank ; suppose it to be produced by the jth weight vector in the ith bank . Then , instead of using Eq . ( 7.3 ) to modify this weight vector , we use the familiar expression w , [ k + 1 ] = w¿ [ k ] + cXk + 1 ( 7 · 4 ) where the ...
... bank ; suppose it to be produced by the jth weight vector in the ith bank . Then , instead of using Eq . ( 7.3 ) to modify this weight vector , we use the familiar expression w , [ k + 1 ] = w¿ [ k ] + cXk + 1 ( 7 · 4 ) where the ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
11 | 30 |
PARAMETRIC TRAINING METHODS | 43 |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components Computer 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 Stanford step subsidiary discriminant Suppose terns theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |