Journal
DECISION SUPPORT SYSTEMS
Volume 46, Issue 2, Pages 481-491Publisher
ELSEVIER
DOI: 10.1016/j.dss.2008.09.001
Keywords
Prior knowledge; Support vector machines; Ellipsoid method; Error bounds; Fat-shattering dimension
Ask authors/readers for more resources
In this study we describe a methodology to exploit a specific type of domain knowledge in order to find tighter error bounds on the performance of classification via Support Vector Machines. The domain knowledge we consider is that the input space lies inside of a specified convex polytope. First, we consider prior knowledge about the domain by incorporating upper and lower bounds of attributes. We then consider a more general framework that allows us to encode prior knowledge in the form of linear constraints formed by attributes. By using the ellipsoid method from optimization literature, we show that, this can be exploited to upper bound the radius of the hyper-sphere that contains the input space, and enables us to tighten generalization error bounds. We provide a comparative numerical analysis and show the effectiveness of our approach. (C) 2008 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available