1) fuzzy least square support vector machine
模糊最小二乘支持向量机
1.
The research of fermentation process modeling based on fuzzy least square support vector machine;
基于模糊最小二乘支持向量机的发酵过程建模研究
2) least squares support vector machine
最小二乘支持向量机
1.
Application of least squares support vector machine within evidence framework in PTA process;
基于证据框架的最小二乘支持向量机在精对苯二甲酸生产中的应用
2.
Pressure sensor temperature compensation based on least squares support vector machine;
基于最小二乘支持向量机的压力传感器温度补偿
3.
Sparse least squares support vector machine;
稀疏最小二乘支持向量机
3) least square support vector machine
最小二乘支持向量机
1.
Forecast of water inrush from coal floor based on least square support vector machine;
基于最小二乘支持向量机的煤层底板突水量预测
2.
Outliers detection in time series of measured data based on least square support vector machine algorithm;
基于最小二乘支持向量机算法的测量数据时序异常检测方法
3.
Image registration based on least square support vector machine;
基于最小二乘支持向量机的图像配准研究
4) least squares support vector machines
最小二乘支持向量机
1.
Coal washery daily water consumption short-term prediction based on least squares support vector machines;
基于最小二乘支持向量机的选煤厂日用水量短期预测
2.
Selection of suitable 3D terrain matching field based on least squares support vector machines;
基于最小二乘支持向量机的三维地形匹配选择
3.
Thermal error prediction of numerical control machine tools based on least squares support vector machines;
基于最小二乘支持向量机的数控机床热误差预测
5) LS-SVM
最小二乘支持向量机
1.
Prediction of hydrogen content in molten aluminum based on LS-SVM;
利用最小二乘支持向量机预测铝熔体氢含量
2.
Time Series Prediction Based on LS-SVM;
基于最小二乘支持向量机的小样本建模方法研究
3.
Research on vibration fault diagnosis of hydro-turbine generating unit based on LS-SVM and information fusion technology;
基于最小二乘支持向量机和信息融合技术的水电机组振动故障诊断研究
6) least square support vector machines
最小二乘支持向量机
1.
Medium and long-term load forecasting based on rough sets and least square support vector machines;
基于粗糙集理论和最小二乘支持向量机的中长期负荷预测
2.
Fuzzy least square support vector machines for regression;
回归型模糊最小二乘支持向量机
3.
Prediction of chaotic time series using least square support vector machines;
混沌时间序列的最小二乘支持向量机预测
补充资料：支持向量机方法
支持向量机(SVM)是90年代中期发展起来的基于统计学习理论的一种机器学习方法，通过寻求结构化风险最小来提高学习机泛化能力，实现经验风险和置信范围的最小化，从而达到在统计样本量较少的情况下，亦能获得良好统计规律的目的。支持向量机算法是一个凸二次优化问题,能够保证找到的极值解就是全局最优解，是神经网络领域域取得的一项重大突破。与神经网络相比，它的优点是训练算法中不存在局部极小值问题，可以自动设计模型复杂度(例如隐层节点数)，不存在维数灾难问题，泛化能力强。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条