In this article we show that there is a strong connection between decision tree learning and local pattern mining. This connection allows us to solve the computationally hard probl...
Decision tree induction techniques attempt to find small trees that fit a training set of data. This preference for smaller trees, which provides a learning bias, is often justifie...
Christian Bessiere, Emmanuel Hebrard, Barry O'Sull...
Mostexisting decision tree systemsuse a greedyapproachto inducetrees -- locally optimalsplits are inducedat every node of the tree. Althoughthe greedy approachis suboptimal,it is ...
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, ...
We develop logical mechanisms and decision procedures to facilitate the verification of full functional properties of inductive tree data-structures using recursion that are soun...
Parthasarathy Madhusudan, Xiaokang Qiu, Andrei Ste...