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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Contours of equal probability density ( 212 – 20122122 + 2z2 = constant ) are ellipses , centered on the origin , whose major axes lie along the line 21 = 22. The eccentricities of the ellipses are equal to 2012 ] Vi + 1012 When 012 is ...
Contours of equal probability density ( 212 – 20122122 + 2z2 = constant ) are ellipses , centered on the origin , whose major axes lie along the line 21 = 22. The eccentricities of the ellipses are equal to 2012 ] Vi + 1012 When 012 is ...
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The conditional density for X , given M , is still given by Eq . ( 3.38 ) . The unconditional density for X can now be obtained by inspection , since Z and M are independent . Then X will have mean y and covariance matrix I + K. That is ...
The conditional density for X , given M , is still given by Eq . ( 3.38 ) . The unconditional density for X can now be obtained by inspection , since Z and M are independent . Then X will have mean y and covariance matrix I + K. That is ...
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Sisältö
Preface vii | 11 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
APPENDIX | 127 |
Tekijänoikeudet | |
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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 discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial known layered machine linear dichotomies 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 specific Stanford step Suppose theorem theory threshold training methods training patterns 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 |