Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Tulokset 1 - 3 kokonaismäärästä 43
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 Yz 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 Yz results in a satisfactory location of the three committee weight vectors .
Sivu 117
What is needed , then , to train PWL machines is a method of adjusting weight vectors and a method for transferring them from bank to bank . Preferably , these training procedures should be iterative so that complicated analyses of ...
What is needed , then , to train PWL machines is a method of adjusting weight vectors and a method for transferring them from bank to bank . Preferably , these training procedures should be iterative so that complicated analyses of ...
Sivu 122
Thus there are R banks of weight vectors , and the ith bank has Li members . 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 ...
Thus there are R banks of weight vectors , and the ith bank has Li members . 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 ...
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Sisältö
I | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
APPENDIX | 127 |
Tekijänoikeudet | |
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Yleiset termit ja lausekkeet
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 |