Advice Refinement in Knowledge-Based Support Vector Machines
Published in Twenty-Fifth Neural Information Processing Systems (NIPS'11), Granada, Spain, 2011
Recommended citation: G. Kunapuli, R. Maclin and J. W. Shavlik. Advice Refinement in Knowledge-Based Support Vector Machines. Twenty-Fifth Neural Information Processing Systems (NIPS'11), Granada, Spain, December 12-16, 2011. http://gkunapuli.github.io/files/11nipsRefinement.pdf
Knowledge-based support vector machines (KBSVMs) incorporate advice from domain experts, which can improve generalization significantly. A major limitation that has not been fully addressed occurs when the expert advice is imperfect, which can lead to poorer models. We propose a model that extends KBSVMs and is able to not only learn from data and advice, but also simultaneously improve the advice. The proposed approach is particularly effective for knowledge discovery in domains with few labeled examples. The proposed model contains bilinear constraints, and is solved using two iterative approaches: successive linear programming and a constrained concave-convex approach. Experimental results demonstrate that these algorithms yield useful refinements to expert advice, as well as improve the performance of the learning algorithm overall.