.png){kind=icon}
返回
Robust kernel-free support vector regression based on optimal margin distribution
.png){kind=icon}
PRE
AI
DOI：10.1016/j.knosys.2022.109477.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Support vector machines have been proven to be useful for regression analysis and forecasting. When stochastic uncertainty is involved in the datasets, robust support vector regression (SVR) models are useful. In this study, we proposed a kernel-free quadratic surface support vector regression (QSSVR) model based on optimal margin distribution (OMD). This model minimizes the variance of the functional margins of all data points to achieve better generalization capability. When the data points exhibit stochastic uncertainty (without the assumption of any specific distribution), the covariance information of noise is employed to construct a robust OMD-based QSSVR (RQSSVR-OMD) model, with a set of probabilistic constraints to ensure its worst-case performance. Moreover, the probabilistic constraints in the proposed model are proven to be equivalently reformulated as second-order cone constraints for efficient implementation. Extensive computational experiments on public benchmark datasets were conducted to demonstrate the superior performance of the proposed RQSSVR-OMD model over other well-established SVR models in terms of forecasting accuracy and time. The proposed model was also validated to successfully handle real-life uncertain battery data for battery power-consumption forecasting. (C) 2022 Elsevier B.V. All rights reserved.
Keyword:
Support vector regression
Optimal margin distribution
Kernel-free SVM
Robust forecasting model
Battery power consumption forecasting
期刊
K
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
Purpura Fulminans and Symmetrical Peripheral Gangrene Caused by Capnocytophaga canimorsus (Formerly DF-2) Septicemia—A Complication of Dog Bite.png)
Medicine
OAAI
MedicineIF0
OAAI分享收藏Antibiotic Prescription Patterns among Children Younger than 5 Years in Nouna District, Burkina Faso
OAAI
OAAI分享收藏Technical analysis strategy optimization using a machine learning approach in stock market indices在股票市场指数中使用机器学习方法进行技术分析策略优化
PREAI
分享收藏Data-driven prediction of battery cycle life before capacity degradation容量退化前电池循环寿命的数据驱动预测NATURE ENERGY
NATURE ENERGYIF60.1
PREAI
分享收藏分享收藏
查看更多内容