Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Tulokset 1 - 3 kokonaismäärästä 13
Sivu 23
2.5 that the absolute value of n⚫ P is the normal Euclidean distance from the origin to the hyperplane . We shall denote this distance by the symbol Aw , which we set equal to wa + 1 / w ) . ( If A > 0 , the origin is on the positive ...
2.5 that the absolute value of n⚫ P is the normal Euclidean distance from the origin to the hyperplane . We shall denote this distance by the symbol Aw , which we set equal to wa + 1 / w ) . ( If A > 0 , the origin is on the positive ...
Sivu 85
That is , W.Y > 0 for all W in W and each Y in y ' Here W is an open convex region bounded by hyperplanes ( the pattern hyperplanes ) all of which pass through the origin . Such a region is called a convex polyhedral cone with vertex at ...
That is , W.Y > 0 for all W in W and each Y in y ' Here W is an open convex region bounded by hyperplanes ( the pattern hyperplanes ) all of which pass through the origin . Such a region is called a convex polyhedral cone with vertex at ...
Sivu 105
The three - dimensional 1 space is then a cube , centered about the origin , whose vertices represent the eight possible combinations of re- sponses of three TLUS . This cube is shown in Fig . 6.6b . If we number the coordinate axes of ...
The three - dimensional 1 space is then a cube , centered about the origin , whose vertices represent the eight possible combinations of re- sponses of three TLUS . This cube is shown in Fig . 6.6b . If we number the coordinate axes of ...
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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 |