Symbolic Knowledge Extraction from Support Vector Machines: A Geometric Approach

被引：0
|
作者：
Ren, Lu [1 ]
Garcez, Artur d'Avila [1 ]
机构：
[1] City Univ London, London EC1V 0HB, England
来源：
ADVANCES IN NEURO-INFORMATION PROCESSING, PT II | 2009年 / 5507卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper presents a new approach to rule extraction from Support Vector Machines (SVMs). SVMs have been applied successfully in many areas with excellent generalization results; rule extraction can offer explanation capability to SVMs. We propose to approximate the SVM classification boundary by solving an optimization problem through sampling and querying followed by boundary searching, rule extraction and post-processing. A theorem and experimental results then indicate that the rules can be used to validate the SVM with high accuracy and very high fidelity.
引用
收藏
页码：335 / 343
页数：9
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