Improved Natural Language Learning via Variance-Regularization Support Vector Machines
Shane Bergsma, Dekang Lin, Dale Schuurmans · 2010
We present a simple technique for learning better SVMs using fewer training examples. Rather than using the standard SVM regularization, we regularize toward low weight-variance. Our new SVM objective remains a convex quadratic function of the weights, and is therefore computationally no harder to optimize than a standard SVM. Variance regularization is shown to enable dramatic improvements in the learning rates of SVMs on three lexical disambiguation tasks. 1