Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 12
Sivu 46
... 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 ( ij ) is assumed to be of the type λ ( ilj ) == 1 — Sij ( 3.5 ) where ...
... 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 ( ij ) is assumed to be of the type λ ( ilj ) == 1 — Sij ( 3.5 ) where ...
Sivu 103
... shown in Fig . 6 · 5 . If it were made too long , it would never be among the weight vectors closest to the hyperplanes which it must eventually cross . Therefore , it would never be adjusted , and W1 and W. would wander around ...
... shown in Fig . 6 · 5 . If it were made too long , it would never be among the weight vectors closest to the hyperplanes which it must eventually cross . Therefore , it would never be adjusted , and W1 and W. would wander around ...
Sivu 106
... shown in Fig . 6 · 7a . In this figure the points marked repre- sent patterns belonging to X1 , and the points marked O represent pat- terns belonging to X2 . Clearly the TLUs in the first layer of the desired layered machine must at ...
... shown in Fig . 6 · 7a . In this figure the points marked repre- sent patterns belonging to X1 , and the points marked O represent pat- terns belonging to X2 . Clearly the TLUs in the first layer of the desired layered machine must at ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding decision surfaces define denote density depends derivation described 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 discriminant functions linear machine linearly separable 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 space Stanford step subsidiary discriminant 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 |