Abstract. Decision trees are considered as an efficient technique to express classification knowledge and to use it. However, their most standard algorithms do not deal with uncert...
We report on a method for compiling decision trees into weighted finite-state transducers. The key assumptions are that the tree predictions specify how to rewrite symbols from an...
We address the issue of compiling ML pattern matching to compact and efficient decisions trees. Traditionally, compilation to decision trees is optimized by (1) implementing decis...
The majority of the existing algorithms for learning decision trees are greedy--a tree is induced top-down, making locally optimal decisions at each node. In most cases, however, ...
The majority of the existing algorithms for learning decision trees are greedy--a tree is induced top-down, making locally optimal decisions at each node. In most cases, however, ...