Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 102
Consider the three pattern vectors and their corresponding pattern hyperplanes ( lines ) shown in Fig . 6 · 5 . The arrows indicate the positive sides of the lines . In this figure is shown the history of weight - vector adjustments ...
Consider the three pattern vectors and their corresponding pattern hyperplanes ( lines ) shown in Fig . 6 · 5 . The arrows indicate the positive sides of the lines . In this figure is shown the history of weight - vector adjustments ...
Sivu 106
The pattern points for this example are 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 ...
The pattern points for this example are 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 ...
Sivu 108
Some examples of nonredundant and redundant partitions are shown in Fig . 6.8 . Note that the partition shown in Fig . 6-7a is also nonredundant . A nonredundant partition is not necessarily one that uses a minimum number of hyperplanes ...
Some examples of nonredundant and redundant partitions are shown in Fig . 6.8 . Note that the partition shown in Fig . 6-7a is also nonredundant . A nonredundant partition is not necessarily one that uses a minimum number of hyperplanes ...
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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 |