Support vector machines with symbolic interpretation

Abstract:

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.

Año de publicación:

2002

Keywords:

• Neural networks
• Prototypes
• Support vector machine classification
• Intelligent Systems
• Artificial Intelligence
• Data Mining
• pbkp_redictive models
• SUPPORT VECTOR MACHINES
• Systems engineering and theory
• Clustering algorithms

Fuente:

scopus