Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 101
k 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 ...
k 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 123
We shall describe the adjustments to be made at the kth step . Suppose that the ( k + 1 ) st ... Then , only this closest weight vector is adjusted and all other weight vectors ( including all those in the other banks ) are left fixed .
We shall describe the adjustments to be made at the kth step . Suppose that the ( k + 1 ) st ... Then , only this closest weight vector is adjusted and all other weight vectors ( including all those in the other banks ) are left fixed .
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adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding covariance decision surfaces define denote density depends derivation Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial layered machine linear dichotomies linear machine linearly separable matrix measurements negative networks 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 selection separable shown side solution space Stanford step Suppose theorem theory threshold training methods 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 |