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 produced by ...
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 produced by ...
Sivu 103
are adjusted as shown since they are the closest to the Y , pattern hyperplane (
they make the two least - negative dot products with Y1 ) . At the next stage ,
examining the weight - vector positions with respect to the Y , pattern hyperplane
we ...
are adjusted as shown since they are the closest to the Y , pattern hyperplane (
they make the two least - negative dot products with Y1 ) . At the next stage ,
examining the weight - vector positions with respect to the Y , pattern hyperplane
we ...
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
Preface vii | 1 |
PARAMETRIC TRAINING METHODS | 43 |
TRAINING THEOREMS | 79 |
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 mean vector 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 Stanford step Suppose theorem theory threshold training methods 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 |