On the approximability of minimizing nonzero variables or unsatisfied relations in linear systems
Theoretical Computer Science
Machine Learning
Sparseness of support vector machines
The Journal of Machine Learning Research
Fast Kernel Classifiers with Online and Active Learning
The Journal of Machine Learning Research
A dual coordinate descent method for large-scale linear SVM
Proceedings of the 25th international conference on Machine learning
The Journal of Machine Learning Research
Hi-index | 0.00 |
When combined with kernels, Support Vector Machines (SVMs) often achieve outstanding accuracy. This comes, however, at the cost of very expensive predictions. To address this issue, we consider the problem of learning a non-linear classifier with a sparsity constraint. Using ℓ1-regularization, we show that this reduces to a univariate parameter search problem, which we show how to solve efficiently. Experiments show that our approach, while leading to much sparser models, is competitive with unconstrained kernel SVMs, both in terms of accuracy and training time.