Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 66
We recall that a TLU implements a hyperplane decision surface which divides the pattern space into two half - spaces . One of these half- spaces is R1 ; the other is R2 . The hyperplane separating these half - spaces is determined by ...
We recall that a TLU implements a hyperplane decision surface which divides the pattern space into two half - spaces . One of these half- spaces is R1 ; the other is R2 . The hyperplane separating these half - spaces is determined by ...
Sivu 104
Thus , each point in the pattern space is trans- formed into one of the vertices of a Pi - dimensional hypercube . This hypercube we shall call the first image space or the I1 space . The trans- formation between the pattern space and ...
Thus , each point in the pattern space is trans- formed into one of the vertices of a Pi - dimensional hypercube . This hypercube we shall call the first image space or the I1 space . The trans- formation between the pattern space and ...
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 |