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Elementary Properties of Estimators
211 4 GaussianUnknown
10 other sections not shown
&NN3 rule algorithm approach assumed asymptotic Bayes estimator bound Chapter classified components convergence covariance matrix decision boundary decision rule decision-directed defined denoted density function dimensionality reduction Dirichlet distribution discussed distance measure distribution E. A. Patrick eigenvalue eigenvectors Euclidean distance example finite mixtures follows IEEE Trans Information Theory known linear loss function Math maximizing mean vector measurement space measurement vectors mixture density nearest neighbor nonlinear nonparametric number of samples observation space parameter point parameter space parameter vector parameters characterizing pattern recognition posteriori density priori knowledge probability density probability density function probability of error problem knowledge procedure properties random regression function relationships respect Section sequence shown in Figure solution statistically independent stochastic approximation subset sufficient statistic Suppose theorem tion tolerance regions training samples training set transformation unknown unsupervised estimation updated vector space zero