Paper Info
Title
Symbolic Knowledge Extraction from Support Vector Machines: A Geometric Approach
Abstract
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.
Year
DOI
Venue
2008
10.1007/978-3-642-03040-6_41
Advances in Neuro-Information Processing
Keywords
Field
DocType
support vector machines,geometric approach,rule extraction,symbolic knowledge extraction,excellent generalization result,explanation capability,optimization problem,high fidelity,new approach,svm classification boundary,high accuracy,knowledge extraction,support vector machine
High fidelity,Pattern recognition,Computer science,Support vector machine,Artificial intelligence,Sampling (statistics),Knowledge extraction,Optimization problem,Machine learning
Conference
Volume
ISSN
Citations 
5507
0302-9743
2
PageRank 
References 
Authors
0.43
9
2
Name
Order
Citations
PageRank
Lu Ren120.43
Artur S. D'avila Garcez243163.57