Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 6
Note that the decision surfaces in the x1 , x2 plane are given by the projections of the intersections of the discriminant functions . Of course , the location and form 6 TRAINABLE PATTERN CLASSIFIERS Discriminant functions,
Note that the decision surfaces in the x1 , x2 plane are given by the projections of the intersections of the discriminant functions . Of course , the location and form 6 TRAINABLE PATTERN CLASSIFIERS Discriminant functions,
Sivu 71
In the third case c is so chosen that the distance moved is some fixed fraction of the original distance of the weight vector from the hyper- plane . For λ = 0 , the weight point is not moved at all ; for λ = 1 , the weight point is ...
In the third case c is so chosen that the distance moved is some fixed fraction of the original distance of the weight vector from the hyper- plane . For λ = 0 , the weight point is not moved at all ; for λ = 1 , the weight point is ...
Sivu 107
Thus , the plane is normal to the vector ( 1,1,1 ) , and the TLU in the second layer which implements this plane gives equal weight to each of the three outputs from the first - layer TLUs . That is , this particular two - layer machine ...
Thus , the plane is normal to the vector ( 1,1,1 ) , and the TLU in the second layer which implements this plane gives equal weight to each of the three outputs from the first - layer TLUs . That is , this particular two - layer machine ...
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Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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adjusted apply assume bank called cells changes Chapter cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described dichotomies discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented important initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors X1 and X2 Y₁ 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 |