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This algorithm inductively learns a set of propositional if...then... rules from a set of training examples. To do this, it performs a general-to-specific beam search through rule-space for the "best" rule, removes training examples covered by that rule, then repeats until no more "good" rules can be found. The original algorithm (Machine Learning Journal paper below) defined "best" using a combination of entropy and a significance test. The algorithm was later improved to replace this evaluation function wite the Laplace estimate, below), and also to induce unordered rule sets as well as ordered rule lists ("decision lists"). The software implements the latest version (ie. using the Laplace heuristic), but has flags which can be set to return it to the original version.

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SunOS, Solaris, IRIX, HP-UX, Linux

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C Compiler

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