2003
JMLR
JMLR 2003
Sparseness of Support Vector Machines
Abstract
Support vector machines (SVMs) construct decision functions that are linear combinations of kernel evaluations on the training set. The samples with non-vanishing coefficients are called support vectors. In this work we establish lower (asymptotical) bounds on the number of support vectors. On our way we prove several results which are of great importance for the understanding of SVMs. In particular, we describe to which "limit" SVM decision functions tend, discuss the corresponding notion of convergence and provide some results on the stability of SVMs using subdifferential calculus in the associated reproducing kernel Hilbert space. [abs] [ pdf ][ ps.gz ][ ps ]
📈
Trend Setter
— Theory
🧭
Keyword Pioneer
— decision function
🐝
Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
🐣
Hot Topic Early Bird
— reproducing kernel hilbert space
Authors
Topics
Machine Learning > Core Methods > Classification
Machine Learning > Optimization & Theory > Learning Theory
Machine Learning > Optimization & Theory > Theory
Machine Learning > Core Methods > Kernel Methods
Machine Learning > Learning Types > Classification
Machine Learning > Core Methods > Support Vector Machine