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From: jair-ed@ptolemy.arc.nasa.gov
Subject: New Article, Using Qualitative Hypotheses to ...
Message-ID: <1995Aug16.203742.9219@ptolemy-ethernet.arc.nasa.gov>
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Date: Wed, 16 Aug 1995 20:37:42 GMT
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JAIR is pleased to announce the publication of the following article:

Zhao, Q. and Nishida, T. (1995)
  "Using Qualitative Hypotheses to Identify Inaccurate Data", 
   Volume 3, pages 119-145.
   PostScript: volume3/zhao95a.ps (626K)
	       compressed, volume3/zhao95a.ps.Z (200K)


   Abstract: Identifying inaccurate data has long been regarded as a
   significant and difficult problem in AI. In this paper, we present a
   new method for identifying inaccurate data on the basis of qualitative
   correlations among related data. First, we introduce the definitions
   of related data and qualitative correlations among related data.  Then
   we put forward a new concept called support coefficient function
   (SCF). SCF can be used to extract, represent, and calculate
   qualitative correlations among related data within a dataset. We
   propose an approach to determining dynamic shift intervals of
   inaccurate data, and an approach to calculating possibility of
   identifying inaccurate data, respectively. Both of the approaches are
   based on SCF. Finally we present an algorithm for identifying
   inaccurate data by using qualitative correlations among related data
   as confirmatory or disconfirmatory evidence. We have developed a
   practical system for interpreting infrared spectra by applying the
   method, and have fully tested the system against several hundred real
   spectra. The experimental results show that the method is
   significantly better than the conventional methods used in many
   similar systems.

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