1) structural risk minimization
结构风险最小化
1.
Study on noise reduction in singular value decomposition based on structural risk minimization;
基于结构风险最小化原则的奇异值分解降噪研究
2.
SVM(Support Vector Machine) is a structural risk minimization principle based on classification algorithm,and it has achieved higher generalization performance with small number of samples than other classification algorithms due to its perfect theoretical properties.
SVM(SupportVectorM ach ine)是一种基于结构风险最小化原则的分类算法,由于其完善的理论基础使其在小样本模式识别中表现出比其他算法更好的泛化能力。
2) structure risk minimization
结构风险最小化
1.
In the model,instead of traditional ERM the principle of structure risk minimization(SRM) is used to fully mine the information of original data.
本文提出一种基于最小二乘支持向量机的中长期负荷组合预测模型,该模型利用结构风险最小化原则代替传统的经验风险最小化,充分挖掘原始数据和单一预测模型的信息,以单一模型的预测数据作为组合预测样本,选择多项式核函数的最小二乘支持向量机进行组合预测。
2.
Support Vector Machine is a learning technology based on structure risk minimization and a predictive tool with better generalization ability,and it effectively solute the fewer samples,nonlinear,high dimension and local minima.
支持向量机是基于结构风险最小化原理的一种学习技术,是一种具有很好泛化能力的预测工具,它有效地解决小样本、非线性、高维数、局部极小等问题。
3.
based on structure risk minimization rule, has attracted widely attention in the area of machine learning.
近年来，基于统计学习理论，兼顾模型的经验风险和置信范围的基于结构风险最小化原则已成为机器学习研究热点之一。
3) structural risk minimization
结构风险最小化原则
1.
SVM is based on the rule of structural risk minimization.
支持向量机是一种突出的小样本数据分析方法,它基于结构风险最小化原则,在一个高维特征空间中构造最优分类超平面,在解决很多实际问题中具有优于其他方法的特点。
2.
Based on structural risk minimization principal,the influences of the error penalty parameter C and the kernel parameter σ on support vector machine s generalization ability are studied.
论文研究了高斯核支持向量机分类在IRIS分类问题上的应用,并结合结构风险最小化原则分析了误差惩罚参数C和高斯核宽度σ对SVM性能的影响,最后通过数值实验进一步分析了这种影响。
4) Structural Risk Minimization
结构风险最小化原理
5) structural risk minimization principle
结构风险最小化原则
1.
The core concept of it was to use the structural risk minimization principle into the modeling process of rbf neutral network so that the rbf function center can be derived by calculate the support vectors of the model.
第一个模型是基于结构风险的灰色补偿神经网络模型(GRBFNN),其核心思想是将结构风险最小化原则应用到RBF神经网络的建模过程中,利用支持向量直接获得RBF函数中心和隐藏层节点数。
2.
Based on the above,the structural risk minimization principle of statistical learning theory based on complex random samples is explored.
结构风险最小化原则是统计学习理论的核心内容之一,是构建支持向量机的重要基础。
3.
Secondly,on the basis of these bounds,the idea of the structural risk minimization principle based on birandom samples is presented.
以这些界为基础,给出基于双重随机样本的结构风险最小化原则。
6) structure-risk-minimum
结构风险最小
1.
Oil and gas prediction by neural networks with structure-risk-minimum.;
神经网络结构风险最小油气预测
补充资料：最小辐亮度与最小辐照度(见核爆炸火球)
最小辐亮度与最小辐照度(见核爆炸火球)
minimum-brightness and minimum-irradiance
zuixiao fuliangdu yu zuixiaofu乙haodu最小辐亮度与最小辐照度(minimum-brightness and而nimum一irradianee)见核爆炸火球。
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