Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 101
weight vectors which are adjusted are those which have dot products closest to zero . ( Ties are resolved arbitrarily . ) These , in one sense , are the easiest to adjust . The adjustment is achieved by the familiar process of adding ...
weight vectors which are adjusted are those which have dot products closest to zero . ( Ties are resolved arbitrarily . ) These , in one sense , are the easiest to adjust . The adjustment is achieved by the familiar process of adding ...
Sivu 103
are adjusted as shown since they are the closest to the Y , pattern hyperplane ( they make the two least - negative ... we see that all of them ( hence , again , the majority ) are on the correct side ; thus , no adjustments are made .
are adjusted as shown since they are the closest to the Y , pattern hyperplane ( they make the two least - negative ... we see that all of them ( hence , again , the majority ) are on the correct side ; thus , no adjustments are made .
Sivu 117
What is needed , then , to train PWL machines is a method of adjusting weight vectors and a method for transferring ... At each pattern presentation the machine may be adjusted by changing weight vectors , by transferring weight vectors ...
What is needed , then , to train PWL machines is a method of adjusting weight vectors and a method for transferring ... At each pattern presentation the machine may be adjusted by changing weight vectors , by transferring weight vectors ...
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adjusted apply assume bank belonging to category called changes Chapter classifier cluster committee components consider consists contains correction corresponding decision surfaces define denote density depends derivation described discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed gi(X given illustrated implemented important initial known layered machine linear dichotomies linear machine linearly separable negative normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem proof properties proved PWL machine quadric reduced regions respect response rule sample mean selected separable shown side space specific Stanford step Suppose theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |