Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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The set ois in general position in a ( d – 1 ) -dimensional space P as a consequence of the general position of X. Therefore , we can use the * To illustrate , let H ; be a hyperplane which partitions X ' and ...
The set ois in general position in a ( d – 1 ) -dimensional space P as a consequence of the general position of X. Therefore , we can use the * To illustrate , let H ; be a hyperplane which partitions X ' and ...
Sivu 72
An extremely simple graphical example of error - correction training is illustrated in Fig . 4.2 . There are four patterns represented by pattern hyperplanes in weight space . The small arrows attached to these planes in this case ...
An extremely simple graphical example of error - correction training is illustrated in Fig . 4.2 . There are four patterns represented by pattern hyperplanes in weight space . The small arrows attached to these planes in this case ...
Sivu 101
6.4 we will illustrate this training procedure for an example in which we have three augmented patterns of two dimensions . 6.4 An example The training procedure described above can be illustrated quite clearly by a two - dimensional ...
6.4 we will illustrate this training procedure for an example in which we have three augmented patterns of two dimensions . 6.4 An example The training procedure described above can be illustrated quite clearly by a two - dimensional ...
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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 |