Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 6
Sivu 28
... quadratic form are called positive definite . If A has one or more of its eigenvalues equal to zero and all the others positive , then the quadratic form will never be negative , and it and A are called positive semidefinite . 2.9 ...
... quadratic form are called positive definite . If A has one or more of its eigenvalues equal to zero and all the others positive , then the quadratic form will never be negative , and it and A are called positive semidefinite . 2.9 ...
Sivu 54
... form to that of Eq . ( 3 · 21 ) . It is the following : 1 p ( X ) = ( 2π ) α / 21⁄2 exp - 2 ( X - M ) ' 1 ( X - M ) } The various ... quadratic form . The surfaces defined 54 PARAMETRIC TRAINING METHODS The multivariate normal distribution,
... form to that of Eq . ( 3 · 21 ) . It is the following : 1 p ( X ) = ( 2π ) α / 21⁄2 exp - 2 ( X - M ) ' 1 ( X - M ) } The various ... quadratic form . The surfaces defined 54 PARAMETRIC TRAINING METHODS The multivariate normal distribution,
Sivu 55
... quadratic form . The surfaces defined by setting this quadratic form equal to constants are hyperellipsoids centered on the point M. These ellipsoids are surfaces of equal probability in the d - dimen- sional pattern space . A set of ...
... quadratic form . The surfaces defined by setting this quadratic form equal to constants are hyperellipsoids centered on the point M. These ellipsoids are surfaces of equal probability in the d - dimen- sional pattern space . A set of ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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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 |