Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 80
A training sequence on y , denoted by Sy , is any infinite sequence of patterns Sy Y1 , Y2 , , . Y. ( 5.2 ) such that , 1 . Each Yk in Sy is a member of Y. 2. Every element of y occurs infinitely often in Sy . The training problem for a ...
A training sequence on y , denoted by Sy , is any infinite sequence of patterns Sy Y1 , Y2 , , . Y. ( 5.2 ) such that , 1 . Each Yk in Sy is a member of Y. 2. Every element of y occurs infinitely often in Sy . The training problem for a ...
Sivu 88
1 2 The rule for generating these sequences is as follows : W ( ) , W , ( ) , ... , WR ( 1 ) are arbitrary initial weight vectors ; Yk belongs to one of the training subsets , say Yi . Then , either ( a ) W ( ) . Yk > W ( k ) .
1 2 The rule for generating these sequences is as follows : W ( ) , W , ( ) , ... , WR ( 1 ) are arbitrary initial weight vectors ; Yk belongs to one of the training subsets , say Yi . Then , either ( a ) W ( ) . Yk > W ( k ) .
Sivu 93
In their theorem , Motzkin and Schoenberg specified a training sequence which is generated recursively from the weight - vector sequence . The ( k + 1 ) th pattern in the training sequence is that member of y ' which has the smallest ...
In their theorem , Motzkin and Schoenberg specified a training sequence which is generated recursively from the weight - vector sequence . The ( k + 1 ) th pattern in the training sequence is that member of y ' which has the smallest ...
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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 Development 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 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 regions respect response rule sample mean selection separable shown side solution space 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 |