Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Tulokset 1 - 3 kokonaismäärästä 27
Sivu 26
Since each of these discriminant functions gi ( X ) is a piecewise linear function of
the components of X we shall call them piecewise linear discriminant functions . *
Any machine employing piecewise linear discriminant functions will be called ...
Since each of these discriminant functions gi ( X ) is a piecewise linear function of
the components of X we shall call them piecewise linear discriminant functions . *
Any machine employing piecewise linear discriminant functions will be called ...
Sivu 112
Since each of the fı , . . . , fp is a linear function of the pattern vector , the
subsidiary discriminant functions are also linear . Thus , according to the
definitions made in Sec . 2 : 7 , a layered machine is a piecewise linear machine .
A layered ...
Since each of the fı , . . . , fp is a linear function of the pattern vector , the
subsidiary discriminant functions are also linear . Thus , according to the
definitions made in Sec . 2 : 7 , a layered machine is a piecewise linear machine .
A layered ...
Sivu 134
Decision regions , 6 convexity of , 20 of a linear machine , 19 , 20 of a piecewise
linear machine , 26 , 27 Error - correction training methods , 69 , 80 for committee
machines , 99 convergence of , 71 , 72 , 75 disadvantages of , 118 graphical ...
Decision regions , 6 convexity of , 20 of a linear machine , 19 , 20 of a piecewise
linear machine , 26 , 27 Error - correction training methods , 69 , 80 for committee
machines , 99 convergence of , 71 , 72 , 75 disadvantages of , 118 graphical ...
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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 |