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2008 NIPS 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.

🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
📈 Trend Setter — Sparse Learning
🧭 Keyword Pioneer — conditional median

Authors

Ingo Steinwart , Andreas Christmann

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

Keywords

sparse learning statistical learning theory epsilon-insensitive loss sparsity support vector machine kernel methods conditional median
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