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Details van artikel 5 van 8 gevonden artikelen
| Titel: |
Detecting Irrelevant Subtrees to Improve Probabilistic Learning from Tree-structured Data |
| Auteur: |
Amaury Habrard Marc Bernard Marc Sebban |
| Verschenen in: |
Fundamenta informaticae |
| Paginering: | Jaargang 66 (2005) nr. 1-2 pagina's 103-130 |
| Jaar: |
2005-06-27 |
| Inhoud: |
In front of the large increase of the available amount of structured data (such as XML documents), many algorithms have emerged for dealing with tree-structured data. In this article, we present a probabilistic approach which aims at a priori pruning noisy or irrelevant subtrees in a set of trees. The originality of this approach, in comparison with classic data reduction techniques, comes from the fact that only a part of a tree (i.e. a subtree) can be deleted, rather than the whole tree itself. Our method is based on the use of confidence intervals, on a partition of subtrees, computed according to a given probability distribution. We propose an original approach to assess these intervals on tree-structured data and we experimentally show its interest in the presence of noise. |
| Uitgever: |
IOS Press |
| Bronbestand: |
Elektronische Wetenschappelijke Tijdschriften |
Details van artikel 5 van 8 gevonden artikelen
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