Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Tulokset 1 - 3 kokonaismäärästä 45
Sivu 103
At the next stage , examining the weight - vector positions with respect to the Y2 pattern hyperplane we see that all of ... Later , another adjustment for Y3 results in a satisfactory location of the three committee weight vectors .
At the next stage , examining the weight - vector positions with respect to the Y2 pattern hyperplane we see that all of ... Later , another adjustment for Y3 results in a satisfactory location of the three committee weight vectors .
Sivu 122
Any training method which adjusts the weight vectors in each bank so that each weight vector finally resides in the center of a cluster of like - category patterns will be called a mode - seeking training method .
Any training method which adjusts the weight vectors in each bank so that each weight vector finally resides in the center of a cluster of like - category patterns will be called a mode - seeking training method .
Sivu 124
Eventually the weight vectors settle down at locations which are centers of gravity of subsets of the patterns . ... second , one must form the average implied by Eq . ( 7.3 ) whenever a weight vector is to be adjusted .
Eventually the weight vectors settle down at locations which are centers of gravity of subsets of the patterns . ... second , one must form the average implied by Eq . ( 7.3 ) whenever a weight vector is to be adjusted .
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Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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Yleiset termit ja lausekkeet
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 |