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 103
are adjusted as shown since they are the closest to the Y1 pattern hyper- plane ( they make the two least - negative dot products with Y1 ) . At the next stage , examining the weight - vector positions with respect to the Y2 pattern ...
are adjusted as shown since they are the closest to the Y1 pattern hyper- plane ( they make the two least - negative dot products with Y1 ) . At the next stage , examining the weight - vector positions with respect to the Y2 pattern ...
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 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern hyperplane pattern space pattern vector pattern-classifying piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space step subsidiary discriminant Suppose terns 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 |
| Alkuperäisteoksen sijainti | Wisconsinin yliopisto - Madison |
| Digitoitu | 16. huhtikuu 2008 |
| ISBN | 0070465703, 9780070465701 |
| Pituus | 137 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |