S. D. Morgera, Computational Complexity and VLSI Implementation of an Optimal Feature Selection Strategy, in: E. S. Gelsma and L. N. Kanal, eds., Pattern Recognition in Practice II (Elsevier Science Publishers B.V., North-Holland, 1986) 389--400. Home/Search Document Not in Database Summary Related Articles |
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Learning Boolean Concepts in the Presence of Many.. - Almuallim, Dietterich (1994) (42 citations) (Correct)
.... [12] and the Bayes classifier [22] Other work (aimed at removing feature redundancy when features are highly correlated) is based on performing a principal components analysis to find a reduced set of new uncorrelated features defined by combining the original features using the eigenvectors [18, 19]. To our knowledge, the problem of finding the smallest subset of Boolean features that is sufficient to construct a consistent hypothesis which is the topic of this paper has not been addressed. 2 Preliminaries For each n 1, let fx 1 ; x 2 ; Delta Delta Delta ; x n g denote a set of n ....
S. D. Morgera, Computational Complexity and VLSI Implementation of an Optimal Feature Selection Strategy, in: E. S. Gelsma and L. N. Kanal, eds., Pattern Recognition in Practice II (Elsevier Science Publishers B.V., North-Holland, 1986) 389--400.
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