Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 46
A special loss function We have shown that an optimum classifying machine
could be achieved by computing and comparing the lx ( i ) . The computations are
particularly simple if the loss function ( ilj ) is assumed to be of the type Milj ) = 1 ...
A special loss function We have shown that an optimum classifying machine
could be achieved by computing and comparing the lx ( i ) . The computations are
particularly simple if the loss function ( ilj ) is assumed to be of the type Milj ) = 1 ...
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 Yi ) . At the next stage ,
examining the weight - vector positions with respect to the Y2 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 Yi ) . At the next stage ,
examining the weight - vector positions with respect to the Y2 pattern hyperplane
we ...
Sivu 106
The pattern points for this example are shown in Fig . 6 . 7a . In this figure the
points marked represent patterns belonging to X1 , and the points marked o
represent patterns belonging to X2 . Clearly the TLUs in the first layer of the
desired ...
The pattern points for this example are shown in Fig . 6 . 7a . In this figure the
points marked represent patterns belonging to X1 , and the points marked o
represent patterns belonging to X2 . Clearly the TLUs in the first layer of the
desired ...
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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 gi(X given implemented important initial layered machine linear dichotomies linear machine linearly separable matrix measurements 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 specific 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 |
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 |
| ISBN | 0070465703, 9780070465701 |
| Pituus | 137 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |