|
| << previous | next >> |
Journal description
All volumes of the corresponding journal
All issues of the corresponding volume
All articles of the corresponding issues
Details for article 5 of 8 found articles
| Title: |
Detecting Irrelevant Subtrees to Improve Probabilistic Learning from Tree-structured Data |
| Author: |
Amaury Habrard Marc Bernard Marc Sebban |
| Appeared in: |
Fundamenta informaticae |
| Paging: | Volume 66 (2005) nr. 1-2 pages 103-130 |
| Year: |
2005-06-27 |
| Contents: |
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. |
| Publisher: |
IOS Press |
| Source file: |
Elektronische Wetenschappelijke Tijdschriften |
Details for article 5 of 8 found articles
| << previous | next >> |