Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Tulokset 1 - 3 kokonaismäärästä 25
Sivu 3
weather - prediction example discussed previously , we might have d = 4 and X1 = 1023 X2 = 1013 X3 = X 4 = 4 - 7 These four numbers might be the current atmospheric pressures ( in millibars ) at stations 1 and 2 and the pressure changes ...
weather - prediction example discussed previously , we might have d = 4 and X1 = 1023 X2 = 1013 X3 = X 4 = 4 - 7 These four numbers might be the current atmospheric pressures ( in millibars ) at stations 1 and 2 and the pressure changes ...
Sivu 75
Y for i = 1 , Ꭱ • ( 4.7 ) Simple extensions of the training procedures already discussed can be used to train a general linear machine . Suppose we have a set y of augmented training patterns divided into subsets Y1 , 2 , . . .
Y for i = 1 , Ꭱ • ( 4.7 ) Simple extensions of the training procedures already discussed can be used to train a general linear machine . Suppose we have a set y of augmented training patterns divided into subsets Y1 , 2 , . . .
Sivu 77
11 The alternative derivation of L ( N , d ) given in the footnote on page 67 follows the derivation by Cameron , 12 The error - correction training procedures discussed in Sec . 4.3 stem from a variety of sources .
11 The alternative derivation of L ( N , d ) given in the footnote on page 67 follows the derivation by Cameron , 12 The error - correction training procedures discussed in Sec . 4.3 stem from a variety of sources .
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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 |