Nov 19, 2016 · In this paper, we proposed a novel two-phase multi-kernel SVR using linear programming method (MK-LP-SVR) for feature sparsification and forecasting.

A novel MK-LP-SVR model is proposed for feature sparsification and prediction.Multi-kernel method reduces dimensionality and gains an interpretable regression.

In this paper, we proposed a novel two-phase multi-kernel SVR using linear programming method (MK-LP-SVR) for feature sparsification and forecasting so as to ...

A novel MK-LP-SVR model is proposed for feature sparsification and prediction.Multi-kernel method reduces dimensionality and gains an interpretable regression.

提出了一种新的MK-LP-SVR模型用于特征稀疏和预测。多核方法降低了维数并获得了可解释的回归。该方法在统计上显着优于SVR和LS-SVR。

A new two-phase model combining Support Vector Regression with information reduction using L1-norm SVR (L1-SVR) is proposed in order to improve the accuracy ...

Two-phase multi-kernel LP-SVR for feature sparsification and forecasting. Z Zhang, G Gao, Y Tian, J Yue. Neurocomputing 214, 594-606, 2016. 11, 2016. A ...

Feb 28, 2022 · To solve highly complex issues of convex quadratic programming in SVR, a novel two-phase MKL-SVR based on linear programming (MK-LP-SVR) was ...

In this paper, we proposed a novel two-phase multi-kernel SVR using linear programming method (MK-LP-SVR) for feature sparsification and forecasting so as to ...

2021. Two-phase multi-kernel LP-SVR for feature sparsification and forecasting. Z Zhang, G Gao, Y Tian, J Yue. Neurocomputing 214, 594-606, 2016. 10, 2016. A ...