The Selection of Effective Attributes for Deciding Between Hypotheses Using Linear Discriminant Functions.
STANFORD UNIV CALIF DEPT OF STATISTICS
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Let X be a multidimensional random variable, whose components are called attributes, with mean mu sub i and covariance matrix Sigma sub i under H sub i, i1, 2. The problem discussed is related to problems in regression theory. Some simple examples illustrate that if Sigma sub 1 is in some sense much smaller than Sigma sub 2, there is a premium on adjoining additional attributes for which the variance under H sub 1 is relatively small.
- Statistics and Probability