Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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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 represent patterns belonging to X1 , and the points marked o represent patterns belonging to X2 . Clearly the TLUs in the first layer of the ...
The pattern points for this example are shown in Fig . 6.7a . In this figure the points marked represent patterns belonging to X1 , and the points marked o represent patterns belonging to X2 . Clearly the TLUs in the first layer of the ...
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
I | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
APPENDIX | 127 |
Tekijänoikeudet | |
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adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding covariance decision surfaces define denote density depends derivation described discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial layered machine linear dichotomies linear machine linearly separable matrix measurements negative normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem proof properties proved PWL machine quadric reduced regions respect response rule sample mean selection separable shown side solution space specific Stanford step Suppose theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors 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 |