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- W49205724 abstract "Whether or not high accuracy classification methods can be scaled to large applications is crucial for the ultimate usefulness of such methods in text categorization. This paper applies two statistical learning algorithms, the Linear Least Squares Fit (LLSF) mapping and a Nearest Neighbor classifier named ExpNet, to a large collection of MEDLINE documents. With the use of suitable dimensionality reduction techniques and efficient algorithms, both LLSF and ExpNet successfully scaled to this very large problem with a result significantly outperforming word-matching and other automatic learning methods applied to the same corpus." @default.
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- W49205724 date "1996-01-01" @default.
- W49205724 modified "2023-10-18" @default.
- W49205724 title "An evaluation of statistical approaches to MEDLINE indexing." @default.
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- W49205724 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/2233015" @default.
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