Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 69
In the error - correction training procedures , the training patterns are presented
to the trainable TLU one at a time for trial . The trial consists of comparing the
actual response of the TLU with the desired response dictated by the category of
the ...
In the error - correction training procedures , the training patterns are presented
to the trainable TLU one at a time for trial . The trial consists of comparing the
actual response of the TLU with the desired response dictated by the category of
the ...
Sivu 75
Suppose that a pattern Y belonging to category i is presented with the result that
some discriminant , say the jth ( j + i ) , is larger than the ith . That is , the machine
erroneously places Y in category j . The weight vectors used by both the ith and ...
Suppose that a pattern Y belonging to category i is presented with the result that
some discriminant , say the jth ( j + i ) , is larger than the ith . That is , the machine
erroneously places Y in category j . The weight vectors used by both the ith and ...
Sivu 123
The following training method is presented because it illustrates several that
have been proposed for mode seeking . No rigorous theoretical treatment has
been advanced to support it , and only limited empirical evidence has been
collected to ...
The following training method is presented because it illustrates several that
have been proposed for mode seeking . No rigorous theoretical treatment has
been advanced to support it , and only limited empirical evidence has been
collected to ...
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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 Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed gi(X given implemented important initial layered machine linear dichotomies linear machine linearly separable matrix measurements networks 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 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 |