Skip to Main Content
ASME Press Select Proceedings

International Conference on Advanced Computer Theory and Engineering (ICACTE 2009)

By
Xie Yi
Xie Yi
Search for other works by this author on:
ISBN:
9780791802977
No. of Pages:
2012
Publisher:
ASME Press
Publication date:
2009

Learning systems are very effective tools for discovering knowledge from databases. Most symbolic learning systems face difficulties when discovering knowledge from large databases. Adapting the learning systems to cope with large databases is the traditional solution to overcome these limitations. This paper evaluates a new methodology for partitioning the representation space. The method selects an irrelevant attribute, determined by a utility function, to partition the representation space. Since irrelevant attributes are not needed to describe concepts learned from the original data, the knowledge discovered from all subspaces should be independent of such an attribute. If the representation space is partitioned by a relevant attribute, the knowledge discovered from all subspaces are combined simply using information about that attribute. The method is analyzed using C5 learning system, or See5.

This content is only available via PDF.
You do not currently have access to this chapter.
Close Modal

or Create an Account

Close Modal
Close Modal