Decision trees have been widely used for online learning classification. Many approaches usually need large data stream to finish decision trees induction, as show notable limitat...
The Prism family of algorithms induces modular classification rules which, in contrast to decision tree induction algorithms, do not necessarily fit together into a decision tree s...
— Reinforcement learning (RL) algorithms have long been promising methods for enabling an autonomous robot to improve its behavior on sequential decision-making tasks. The obviou...
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...
Expert systems (ESs) are complex information systems that are expensive to build and difficult to validate. Numerous knowledge representation strategies such as rules, semantic net...
In this paper we present an improvement of the precision of classification algorithm results. Two various approaches are known: bagging and boosting. This paper describes a set of ...
While decision trees have been used primarily for classification, they can also model regression or function approximation. Like classification trees, regression trees often yield...
This study demonstratesthe use of decision tree classifiers as the basis for a general gene-finding system. The system uses a dynamic programmingalgorithm that. finds the optimal ...
Steven Salzberg, Xin Chen, John Henderson, Kenneth...
Our goal is to identify the features that predict the occurrence and placement of discourse cues in tutorial explanations in order to aid in the automatic generation of explanatio...
Barbara Di Eugenio, Johanna D. Moore, Massimo Paol...