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FORMULATION OF THE CLASSIFICATION
CHAPTER H STATEMENT OF THE TRAINING PROBLEM
BASIC CONCEPTS AND TECHNIQUES
2 other sections not shown
adaptive clustering algorithm Adaptive Clustering Techniques associated samples associated training vectors automatic classification system bivariate normal clusters body of unit classification decisions cluster CG cluster covariance matrix cluster localization cluster mean vector cluster parameters cluster population clusters of training components constant probability density constraints Contours of Constant core storage requirements Correlated or uncorrelated Discriminant Function Evaluators ellipse Elliptical Contours estimate of cluster Euclidean distance Euclidean vector space expected value Figure Gaussian clusters gonal increasing values initial estimate iterative localization and sample multivariate normal clusters multivariate normal distributions multivariate normal probability non-Gaussian normal probability density number of clusters number of samples Pattern Recognition probability density function random variables representative resultant force vector sample association algorithm sample association process sample vectors separation distance set of measurements set of sample set of training training samples training set unit mass Univariate Normal Distribution University of California unknown class vector and covariance vector field approach