This paragraph is from the book Machine Learning by Tom M.Mitchell (Page 26):

  1. Initialize $h$ to the most specific hypothesis in $H$
  2. For each positive training instance $x$
    $\;\;\;\;\;\;$.For each attribute constraint $a_i$, in $h$
    $\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;$If the constraint $a_i$, is satisfied by $x$
    $\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;$Then do nothing
    $\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;$Else replace $a_i$, in $h$ by the next more general constraint that
    $\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;$is satisfied by $x$
  3. Output hypothesis h

Now, If there is more than one maximally specific hypothesis consistent with the set $D$ of training examples, Does the order of iteration on the training examples affect the answer returned by this so-called FIND-S algorithm? I think the answer would be yes but I haven't been able to find an example yet.



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