Safe and Efficient Screening For Sparse Support Vector Machine
arXiv:1310.8320v110 citations
Originality Synthesis-oriented
AI Analysis
This work addresses efficiency improvements for researchers and practitioners using sparse SVMs, but it appears incremental as it builds on existing screening methods.
The authors tackled the problem of speeding up training for sparse support vector machines by developing a screening technique based on variational inequality, which is both efficient and safe in removing inactive features.
Screening is an effective technique for speeding up the training process of a sparse learning model by removing the features that are guaranteed to be inactive the process. In this paper, we present a efficient screening technique for sparse support vector machine based on variational inequality. The technique is both efficient and safe.