Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 54
... column vector representing the pattern . is ad X1 column vector . It has the property of being equal to the expected value of X ( i.e. , M = E [ X ] ) and is therefore called the mean vector . ma Old σ11 012 σij Odd σdi is a symmetric ...
... column vector representing the pattern . is ad X1 column vector . It has the property of being equal to the expected value of X ( i.e. , M = E [ X ] ) and is therefore called the mean vector . ma Old σ11 012 σij Odd σdi is a symmetric ...
Sivu 108
... columns of M be a column of zeros . Let us delete this column from M to form a square P × P matrix M. Each column of M is a vertex belonging to either ( 1 ) or ( 1 ) . * In the following proof we do not make use of the fact that the ...
... columns of M be a column of zeros . Let us delete this column from M to form a square P × P matrix M. Each column of M is a vertex belonging to either ( 1 ) or ( 1 ) . * In the following proof we do not make use of the fact that the ...
Sivu 109
... column of M is a vertex belonging to if the ith column of ✩ is a vertex belonging to 2 ( 1 ) ( 6 · 10 ) Since ✩ has an inverse ( it has rank equal to P ) , we can always solve for w by W = CM - 1 ( 6.11 ) Thus , since a threshold and ...
... column of M is a vertex belonging to if the ith column of ✩ is a vertex belonging to 2 ( 1 ) ( 6 · 10 ) Since ✩ has an inverse ( it has rank equal to P ) , we can always solve for w by W = CM - 1 ( 6.11 ) Thus , since a threshold and ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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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 |