1)LSHS-MCSVM最小二乘超球多类支持向量机
1.As a result,a kind of new multi-class classifiers,Least Square Hyper-Sphere Multi-Class SVM(LSHS-MCSVM),was proposed.超球体多类支持向量机(HSMC-SVM)是一种直接型多类分类器,具有训练速度快,检测效率高的优点,但由于HSMC-SVM使用一阶范数软间隔作为目标函数的惩罚项,使得其训练精度受到一定影响,为了提高HSMC-SVM训练精度,将最小二乘法引入到HSMC-SVM中,提出了最小二乘超球多类支持向量机(LSHS-MCSVM)的概念,并且分析了它的训练算法和判决规则,从而形成了完整的LSHS-MCSVM分类理论。
2)multi least square-support vector machine多最小二乘支持向量机
3)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
英文短句/例句
1.Study on Least Squares Support Vector Machine and Its Applications;最小二乘支持向量机算法及应用研究
2.Improved Fuzzy Least Squares Support Vector Machines Model改进的模糊最小二乘支持向量机模型
3.Predictive Control Based on Least Squares Support Vector Machine基于最小二乘支持向量机的预测控制
4.LSSVM-IMC control for ship course-keeping system船舶航向最小二乘支持向量机内模控制
5.Prediction of Melt Index Based on Wavelet and LS-SVM基于小波-最小二乘支持向量机的熔融指数预测
6.Soft Sensor Modeling Based on Chaos Optimization Algorithm and Least Squares Support Vector Machines基于混沌最小二乘支持向量机的软测量建模
7.Fault diagnosis for locomotive bearings based on least squares support vector machine基于最小二乘支持向量机的机车轴承故障诊断
8.Research on Least Squares Support Vector Machine Power System Short-Term Load Forecasting;最小二乘支持向量机短期负荷预测研究
9.Least Squares Support Vector Machines of Fitting B-spline Surface;最小二乘支持向量机对数据点的B样条拟合
10.Forecasting Research of Communicable Diseases Based on Least Squares Support Vector Machine基于最小二乘支持向量机的传染病预测与研究
11.Error prediction based on least squares support vector machines基于最小二乘支持向量机的故障预测法
12.Implementation of Least Square Support Vector Machine for Prediction最小二乘支持向量机回归预测模型研究与实现
13.A blurred image restoration method based on LS-SVM基于最小二乘支持向量机的模糊图像恢复
14.Adaptive Weighted Least Square Support Vector Machine Regression and Its Application自适应加权最小二乘支持向量机回归及应用
15.Research on Prediction Model of Traffic Safety Using Least Square Support Vector Machine基于最小二乘支持向量机的交通安全预测模型
16.Extraction of Fetal Electrocardiogram Signal Using Least Squares Support Vector Machines基于最小二乘支持向量机的胎儿心电信号提取
17.Classification of Music Instruments Based on LS-SVM基于最小二乘支持向量机的乐器音乐分类
18.Genetic-least square support vector machine estimation of slope stability进化-最小二乘支持向量机的边坡稳定性估计
相关短句/例句
multi least square-support vector machine多最小二乘支持向量机
3)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
4)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
5)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
6)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
延伸阅读
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。
