Support vector machines with symbolic interpretation
H. Nunez, Cecilio Ángulo, Andreu Catalá · 2003
In this work, a procedure for rule extraction from support vector machines (SVMs) is proposed. Our method first determines the prototype vectors by using k-means. Then, these vectors are combined with the support vectors using geometric methods to define ellipsoids in the input space, which are later translated to if-then rules. In this way, it is possible to give an interpretation to the knowledge acquired by the SVM. On the other hand, the extracted rules render possible the integration of SVMs with symbolic AI systems.