Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 88
2 The rule for generating these sequences is as follows : W ( 1 ) , W , ( ! ) , WR ( 1 ) are arbitrary initial weight vectors ; Yx belongs to one of the training subsets , say Yi . Then , either ( a ) W. ( ) . Yk > W ( k ) .
2 The rule for generating these sequences is as follows : W ( 1 ) , W , ( ! ) , WR ( 1 ) are arbitrary initial weight vectors ; Yx belongs to one of the training subsets , say Yi . Then , either ( a ) W. ( ) . Yk > W ( k ) .
Sivu 89
The next step in the proof is to form from the reduced training sequence Sy and the reduced weight - vector sequences SW , . SW a corresponding sequence of vectors from the set z . Let us denote this sequence of vectors from Z by the ...
The next step in the proof is to form from the reduced training sequence Sy and the reduced weight - vector sequences SW , . SW a corresponding sequence of vectors from the set z . Let us denote this sequence of vectors from Z by the ...
Sivu 90
The sequence Sz can be regarded as a reduced training sequence of the patterns in Z , and since Z is linearly contained , the application of the fixed - increment procedure must result , by Theorem 5.1 , in a solution weight vector .
The sequence Sz can be regarded as a reduced training sequence of the patterns in Z , and since Z is linearly contained , the application of the fixed - increment procedure must result , by Theorem 5.1 , in a solution weight vector .
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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 |