Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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... covariance matrix of estimation ( prediction ) error for ith observer covariance matrix of estimation ( prediction ) error for ith observer steady - state covariance matrix of estimation ( prediction ) errors em ( k ) and en ( k ) of ...
... covariance matrix of estimation ( prediction ) error for ith observer covariance matrix of estimation ( prediction ) error for ith observer steady - state covariance matrix of estimation ( prediction ) errors em ( k ) and en ( k ) of ...
Sivu 178
... covariance matrices. Biometrika, 97, 539–550. — (2010b). Sparse multivariate regression with covariance estimation. J Comput Graph Stat, 19, 947–962. Rothman AJ, Levina E, Zhu J. (2009). Generalized thresholding of large covariance ...
... covariance matrices. Biometrika, 97, 539–550. — (2010b). Sparse multivariate regression with covariance estimation. J Comput Graph Stat, 19, 947–962. Rothman AJ, Levina E, Zhu J. (2009). Generalized thresholding of large covariance ...
Sivu 272
... covariance matrix one-period ahead. In the case of the copula-dependence models, the variance and covariance terms are forecasted separately based on the specifications chosen for the marginals (GJR-GARCH, RGARCH or RRGARCH) and for the ...
... covariance matrix one-period ahead. In the case of the copula-dependence models, the variance and covariance terms are forecasted separately based on the specifications chosen for the marginals (GJR-GARCH, RGARCH or RRGARCH) and for the ...
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 |