2008
NeurIPS
NeurIPS 2008
Sparsity of SVMs that use the epsilon-insensitive loss
Abstract
In this paper lower and upper bounds for the number of support vectors are derived for support vector machines (SVMs) based on the epsilon-insensitive loss function. It turns out that these bounds are asymptotically tight under mild assumptions on the data generating distribution. Finally, we briefly discuss a trade-off in epsilon between sparsity and accuracy if the SVM is used to estimate the conditional median.
📈
Trend Setter
- Sparse Learning
🐣
Hot Topic Early Bird
- sparsity
Authors
Topics
Machine Learning > Core Methods > Classification
Machine Learning > Core Methods > Regression
Machine Learning > Optimization & Theory > Learning Theory
Machine Learning > Optimization & Theory > Optimization
Machine Learning > Optimization & Theory > Statistical Learning
Machine Learning > Core Methods > Support Vector Machine
Machine Learning > Learning Types > Sparse Learning