Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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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 calculation ...
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 calculation ...
Sivu 52
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 zero ,
the ...
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 zero ,
the ...
Sivu 59
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 ,
P ( X ) ...
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 ,
P ( X ) ...
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Sisältö
Preface vii | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding covariance 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 machine linearly separable matrix 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 solution space Stanford step Suppose theorem theory threshold training methods training procedure training sequence training subsets transformation values weight vectors zero
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 |
| Pituus | 137 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |