Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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3.6 The bivariate normal probability - density function In the example of Sec . 3.5 , we assumed that the pattern components were statistically independent , binary , random variables . Such an assumption permitted a straightforward ...
3.6 The bivariate normal probability - density function In the example of Sec . 3.5 , we assumed that the pattern components were statistically independent , binary , random variables . Such an assumption permitted a straightforward ...
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3.7 The multivariate normal distribution The matrix notation of the last section has a direct parallel in the case in which the number of pattern components is greater than two . There is a multivariate normal distribution which ...
3.7 The multivariate normal distribution The matrix notation of the last section has a direct parallel in the case in which the number of pattern components is greater than two . There is a multivariate normal distribution which ...
Sivu 131
A 3 Transformation of normal patterns Consider the normal distribution expressed by where p ( X ) = 1 - - ( 2π ) d / 2 | | 31⁄2 exp { — 1⁄2 [ ( X — M ) ' Σ − 1 ( X — M ) ] } ( A.10 ) is the covariance matrix and M is the mean vector .
A 3 Transformation of normal patterns Consider the normal distribution expressed by where p ( X ) = 1 - - ( 2π ) d / 2 | | 31⁄2 exp { — 1⁄2 [ ( X — M ) ' Σ − 1 ( X — M ) ] } ( A.10 ) is the covariance matrix and M is the mean vector .
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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 |