Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Tulokset 1 - 3 kokonaismäärästä 54
Sivu 72
An extremely simple graphical example of error - correction training is illustrated in Fig . 4-2 . There are four patterns represented by pattern hyperplanes in weight space . The small arrows attached to these planes in this case ...
An extremely simple graphical example of error - correction training is illustrated in Fig . 4-2 . There are four patterns represented by pattern hyperplanes in weight space . The small arrows attached to these planes in this case ...
Sivu 101
6-4 we will illustrate this training procedure for an example in which we have three augmented patterns of two dimensions . 6.4 An example The training procedure described above can be illustrated quite clearly by a two ...
6-4 we will illustrate this training procedure for an example in which we have three augmented patterns of two dimensions . 6.4 An example The training procedure described above can be illustrated quite clearly by a two ...
Sivu 105
For example , pat- terns 3 and 7 both yield a response of +1 for TLU 1 and a response of −1 for the other TLUS ; hence these two patterns are transformed into the single point ( 1 , -1 , -1 ) in image space .
For example , pat- terns 3 and 7 both yield a response of +1 for TLU 1 and a response of −1 for the other TLUS ; hence these two patterns are transformed into the single point ( 1 , -1 , -1 ) in image space .
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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 |