Simultaneous Safe Feature and Sample Elimination for Sparse Support Vector Regression.
IEEE Transactions on Signal Processing(2019)
摘要
Sparse support vector regression (SSVR) is an effective regression technique. It has been successfully applied to many practical problems. However, it remains challenging to handle the large-scale problems. A nice property of SSVR is double sparsity in the sense that most irrelevant features and samples have no effect on the regressor. Inspired by it, we propose a simultaneous safe feature and sam...
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关键词
Support vector machines,Acceleration,Training,Computational modeling,Signal processing algorithms,Safety,Linear programming
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