Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 58
i where N , is the number of patterns in the training subset X ;; ( X ) ; is called the sample mean ( or center of gravity ) of the ith category , and ( Σ ) ; is called the sample covariance matrix of the ith category .
i where N , is the number of patterns in the training subset X ;; ( X ) ; is called the sample mean ( or center of gravity ) of the ith category , and ( Σ ) ; is called the sample covariance matrix of the ith category .
Sivu 60
By expanding the exponent in Eq . ( 3 · 46 ) , it is a straightforward matter to identify the mean vector and ... K + -1 μ + K ( K + Σ ΤΙ Σ N EN ( K ) -1 ( X ) N ( 3.49 ) and the sample mean = X ; N ( 3.50 60 PARAMETRIC TRAINING METHODS.
By expanding the exponent in Eq . ( 3 · 46 ) , it is a straightforward matter to identify the mean vector and ... K + -1 μ + K ( K + Σ ΤΙ Σ N EN ( K ) -1 ( X ) N ( 3.49 ) and the sample mean = X ; N ( 3.50 60 PARAMETRIC TRAINING METHODS.
Sivu 61
and the sample mean = X ; N ( 3.50 ) The optimum a posteriori discriminant function ( after training on the set { X1 , X2 , . . . , XN ) is then given by Eq . ( 3 · 31 ) with M replaced by us and replaced by + KN .
and the sample mean = X ; N ( 3.50 ) The optimum a posteriori discriminant function ( after training on the set { X1 , X2 , . . . , XN ) is then given by Eq . ( 3 · 31 ) with M replaced by us and replaced by + KN .
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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 |