Paper: Improved Natural Language Learning via Variance-Regularization Support Vector Machines

ACL ID W10-2921
Title Improved Natural Language Learning via Variance-Regularization Support Vector Machines
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2010
Authors

We present a simple technique for learn- ing better SVMs using fewer training ex- amples. Rather than using the standard SVM regularization, we regularize toward low weight-variance. Our new SVM ob- jective remains a convex quadratic func- tion of the weights, and is therefore com- putationally no harder to optimize than a standard SVM. Variance regularization is shown to enable dramatic improvements in the learning rates of SVMs on three lex- ical disambiguation tasks.