Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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First , however , a few words are in order about a key assumption under* We assume that the response is a ... Pattern classifiers with random responses have been discussed occasionally in the literature , but our treatment shall not ...
First , however , a few words are in order about a key assumption under* We assume that the response is a ... Pattern classifiers with random responses have been discussed occasionally in the literature , but our treatment shall not ...
Sivu 72
Note that an adjustment to correct the response for one pattern may very well undo a correction made on a previous pattern . Eventually , however , one last correction will be made that does not affect the correct response to the other ...
Note that an adjustment to correct the response for one pattern may very well undo a correction made on a previous pattern . Eventually , however , one last correction will be made that does not affect the correct response to the other ...
Sivu 98
way that the consensus or majority of the TLU responses is correct for each pattern . The consensus can be polled by ... The second layer consists of the vote - taking TLU whose response is the majority response of the committee TLUs .
way that the consensus or majority of the TLU responses is correct for each pattern . The consensus can be polled by ... The second layer consists of the vote - taking TLU whose response is the majority response of the committee TLUs .
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Sisältö
I | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
APPENDIX | 127 |
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 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 |