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 ( or
subtracting ) ...
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 ( or
subtracting ) ...
Sivu 103
are adjusted as shown since they are the closest to the Y , pattern hyperplane (
they make the two least - negative dot products with Y1 ) . At the next stage ,
examining the weight - vector positions with respect to the Y , pattern hyperplane
we ...
are adjusted as shown since they are the closest to the Y , pattern hyperplane (
they make the two least - negative dot products with Y1 ) . At the next stage ,
examining the weight - vector positions with respect to the Y , pattern hyperplane
we ...
Sivu 123
Then , only this closest weight vector is adjusted and all other weight vectors (
including all those in the other banks ) are left fixed . The adjustment made to this
closest weight vector is to move it part way along a line directed toward the
pattern ...
Then , only this closest weight vector is adjusted and all other weight vectors (
including all those in the other banks ) are left fixed . The adjustment made to this
closest weight vector is to move it part way along a line directed toward the
pattern ...
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Sisältö
Preface vii | 1 |
PARAMETRIC TRAINING METHODS | 43 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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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 described 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 mean vector measurements 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 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
Kirjaluettelon tiedot
| Otsikko | Learning Machines: Foundations of Trainable Pattern-classifying Systems McGraw-Hill series in systems science |
| Kirjoittaja | Nils J. Nilsson |
| Painos | kuvitettu |
| Kustantaja | McGraw-Hill, 1965 |
| Pituus | 137 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |