Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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To explain the more important implementation we first define the M - dimensional vector F whose components f1 , f2 , , fM are functions d . The first d components of F are x1 , x22 , 1 ) / 2 components are all the pairs x1x2 , X1X 3 ...
To explain the more important implementation we first define the M - dimensional vector F whose components f1 , f2 , , fM are functions d . The first d components of F are x1 , x22 , 1 ) / 2 components are all the pairs x1x2 , X1X 3 ...
Sivu 50
ties might then be : Pi = qi = number of typical patterns belonging to category 1 for which the ith component equals one N1 ... 3.5 , we assumed that the pattern components were statistically independent , binary , random variables .
ties might then be : Pi = qi = number of typical patterns belonging to category 1 for which the ith component equals one N1 ... 3.5 , we assumed that the pattern components were statistically independent , binary , random variables .
Sivu 111
Since U is a binary vector with P components there are 2o distinct U vectors . For some of these U vec- tors , H ( U ) = 1 , and for the remaining , H ( U ) = -1 . Let H1 be a matrix whose rows consist of those U vectors for which H ( U ) ...
Since U is a binary vector with P components there are 2o distinct U vectors . For some of these U vec- tors , H ( U ) = 1 , and for the remaining , H ( U ) = -1 . Let H1 be a matrix whose rows consist of those U vectors for which H ( U ) ...
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Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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Yleiset termit ja lausekkeet
adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding 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 discriminant functions linear machine linearly separable 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 reduced regions respect response rule sample mean selection separable shown side space Stanford step subsidiary discriminant 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 |