Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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The first d weights employed by the ith discriminator are given by the values of the components of the transformed mean vector , E - M ;; the ( d + 1 ) th weight is given by the value of the constant , log pi – 12M ; * £ -M ; If R = 2 ...
The first d weights employed by the ith discriminator are given by the values of the components of the transformed mean vector , E - M ;; the ( d + 1 ) th weight is given by the value of the constant , log pi – 12M ; * £ -M ; If R = 2 ...
Sivu 104
For a given set of weights , the first layer will transform a finite set X of pattern vectors into a finite set g ( 1 ) of image - space vertices . Now looking at the second layer of TLUs , we can say that it transforms the vertices of ...
For a given set of weights , the first layer will transform a finite set X of pattern vectors into a finite set g ( 1 ) of image - space vertices . Now looking at the second layer of TLUs , we can say that it transforms the vertices of ...
Sivu 121
The nonparametric decision rule to be described assumes the existence of a method to find good estimates for these modes , given the training subsets . Suppose the modes for the various categories , as established by a training ...
The nonparametric decision rule to be described assumes the existence of a method to find good estimates for these modes , given the training subsets . Suppose the modes for the various categories , as established by a training ...
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adjusted apply assume bank belonging to category called changes Chapter classifier cluster committee components consider consists contains correction corresponding decision surfaces define denote density depends derivation described discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed gi(X given illustrated implemented important initial known layered machine linear dichotomies linear machine linearly separable 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 selected separable shown side 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 |