Recycling decision trees in numeric domains

Research output: Contribution to journalArticle

Abstract

A decision tree's classification performance can drop if the tree is used in a changed context such as different accent in speach recognition. This brittleness can partially be rectified by the use of a cheap second tier implemented as a linear classifier. The transfer of the tree to a novel context is accomplished by re-inducing the second tier, without the need to re-induce the more expensive first tier. Experiments reported in this paper indicate that quick adaptation to the target context can indeed be achieved.

Original languageEnglish (US)
Pages (from-to)195-204
Number of pages10
JournalInformatica (Ljubljana)
Volume24
Issue number2
StatePublished - Jun 1 2000

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ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Computer Science Applications
  • Artificial Intelligence

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