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 such that Sy = Y1 , Y 2 , Yk , • 1. Each Y in Sy is a member of y . 2. Every element of y occurs infinitely often in Sy . ( 5.2 ) The training problem for a ...
A training sequence on y , denoted by Sy , is any infinite sequence of patterns such that Sy = Y1 , Y 2 , Yk , • 1. Each Y in Sy is a member of y . 2. Every element of y occurs infinitely often in Sy . ( 5.2 ) The training problem for a ...
Sivu 89
The first step is to generate a new set Z of higher - dimensional vectors from the training set y . ... The next step in the proof is to form from the reduced training sequence Sŷ and the reduced weight - vector sequences Sŵ1 , SWR a ...
The first step is to generate a new set Z of higher - dimensional vectors from the training set y . ... The next step in the proof is to form from the reduced training sequence Sŷ and the reduced weight - vector sequences Sŵ1 , SWR a ...
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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Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
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adjusted apply assume bank called cells changes Chapter cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described dichotomies discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented important initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors X1 and X2 Y₁ 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 |