Abstract
A decision tree-based system for learning from numeric data is described. Results from linear algebra (pseudoinverse matrices) help the system to generate decision trees where the nodes are represented by linear threshold units minimizing the mean square error. The system's capability to provide good classifications with small decision trees is demonstrated on artificial and benchmark data.
