Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 12
Sivu 52
... origin . Contours of equal probability density ( z12-20122122 + 222 = constant ) are ellipses , cen- tered on the origin , whose major axes lie along the line z1 = 22. The eccentricities of the ellipses are equal to 2012 VI + 1012 When ...
... origin . Contours of equal probability density ( z12-20122122 + 222 = constant ) are ellipses , cen- tered on the origin , whose major axes lie along the line z1 = 22. The eccentricities of the ellipses are equal to 2012 VI + 1012 When ...
Sivu 85
... origin but at some point whose distance from the origin increases with increasing M and b . A two- dimensional example is shown in Fig . 5.1 . Let WW2 be the squared distance between some fixed interior point W in w ' and Ŵ ,, where Ŵ ...
... origin but at some point whose distance from the origin increases with increasing M and b . A two- dimensional example is shown in Fig . 5.1 . Let WW2 be the squared distance between some fixed interior point W in w ' and Ŵ ,, where Ŵ ...
Sivu 105
... origin , whose vertices represent the eight possible combinations of re- sponses of three TLUs . This cube is shown in Fig . 6 · 66 . If we number the coordinate axes of the image - space cube in accordance with the TLU TLU 3 TLU 2 5 S ...
... origin , whose vertices represent the eight possible combinations of re- sponses of three TLUs . This cube is shown in Fig . 6 · 66 . If we number the coordinate axes of the image - space cube in accordance with the TLU TLU 3 TLU 2 5 S ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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 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 discriminant functions 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 Stanford step subsidiary discriminant Suppose theorem theory threshold training methods 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 |