Comment author:jstults
17 February 2010 07:29:38PM
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<blockquote>On the basis of these remarks I submit the following qualified statement: while the belief network paradigm is mathematically elegant and intuitively appealing, it is NOT very useful for describing real data.</blockquote>
The challenge is just as wrong; to <A href="http://en.wikipedia.org/wiki/Artificial_intelligence">quote from the wiki</a>:
<blockquote>Classifier performance depends greatly on the characteristics of the data to be classified. There is no single classifier that works best on all given problems; this is also referred to as the "no free lunch" theorem. Determining a suitable classifier for a given problem is still more an art than science.</blockquote>
Russell and Norvig, 1st ed. has a good example comparing the performance of a Bayes net with a decision tree on data that was generated by a decision tree-like process, of course the net did not perform as well as a decision tree on that data, surprise, surprise.
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<blockquote>On the basis of these remarks I submit the following qualified statement: while the belief network paradigm is mathematically elegant and intuitively appealing, it is NOT very useful for describing real data.</blockquote>
The challenge is just as wrong; to <A href="http://en.wikipedia.org/wiki/Artificial_intelligence">quote from the wiki</a>: <blockquote>Classifier performance depends greatly on the characteristics of the data to be classified. There is no single classifier that works best on all given problems; this is also referred to as the "no free lunch" theorem. Determining a suitable classifier for a given problem is still more an art than science.</blockquote>
Russell and Norvig, 1st ed. has a good example comparing the performance of a Bayes net with a decision tree on data that was generated by a decision tree-like process, of course the net did not perform as well as a decision tree on that data, surprise, surprise.