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- structural risk minimum 结构风险最小化
- structure risk minimum 结构风险最小化
- structure risk minimum (SRM) 结构风险最小化
- A much greater structural risk occurs in temperate and tropical areas. 在温带和热带地区会出现更大的结构风险。
- The method takes advantages of the minimum structure risk of SVM and the quickly globally optimizing ability of PSO for soft sensor modeling. 该方法利用支持向量机结构风险最小化原则和PSO算法快速全局优化的特点，用于软测量建模。
- An algorithm is presented through using structural risk minimization (SRM) based on statistical learning theory. 在研究统计学理论的基础上，提出了以结构风险最小化为目标的训练方法。
- The structure of WNN is trained using Structural Risk Minimization(SR... 实验结果表明,该预测模型具有预测精度高,使用方便等优点。
- Based on modern portfolio selection theory,investors look for theportfolio of return maximum and risk minimum if the correlations ofdifferent stock returns are smaller in international markets than in thedomestic markets. 现代资产组合理论(Modern Portfolio Selection Theory)认为投资者所追求的是报酬最大且风险最小的投资组合,即从理论上讲,当国际股市收益率之间的相关性小于国内股市时,投资者就可以在全球范围内进行资产组合投资。
- Support vector machine is a learning technique based on the structural risk minimization principle as well as a new regression method with good generalization ability. 支持向量机是一种基于结构风险最小化原理的学习技术,也是一种新的具有很好泛化性能的回归方法。
- Support vector machine(SVM) is a novel and powerful learning method which is derived based on statistical learning theory(SLT) and the structural risk minimization principle. 建立在统计学习理论基础之上的支持向量机(SVM),是一种基于结构风险最小的小样本机器学习方法。
- An advanced subspace-partition based fuzzy system model (SPFS) is proposed to realize structural risk minimization (SRM) in the modeling of fuzzy system. 摘要针对模糊系统建模过程中实现结构风险最小化问题，提出改进的基于子空间划分的模糊系统模型(SPFS)。
- Support vector machine (SVM) is a technique of classification based on the structural risk minimization principle, and a regression method with fine ability of generalization. 摘要支持向量机(SVM)是一种基于结构风险最小化原理的分类技术，也是一种具有很好泛化性能的回归分析方法。
- Support vector machine (SVM) is a learning algorithm based on structural risk minimization (SRM), it is a good regression way, which has good generalization ability also. 支持向量机(SVM)是一种基于结构风险最小化原理(SRM)的学习算法,也是一种具有很好的泛化性能的回归方法。
- The support vector machine is a new statistical learning method. It can solve small-sample, non-linear and high dimension problems by using structural risk minimization (SRM) instead of empirical risk minimization (ERM). 支持向量机则是一门新的统计模式分类方法,支持向量机用结构风险最小化原则代替了经验风险最小化原则,同传统的模式识别方法相比,支持向量机在小样本、非线性及高维模式识别问题中表现出许多特有的优势。
- Support vector machines (SVM) is a new general machine-learning tool based on the structural risk minimization principle.It exhibits good generalization when fault samples are few. 摘要支持向量机(SVM)是一种基于统计学习理论的新型机器学习方法，对小样本决策具有较好的学习推广性。
- Based on statistical learning theory, the SVM algorithm embodies the structural risk minimization (SRM) principle, which is more rapid more accurate, and has higher generalized performance. SVM回归法采用结构风险最小化准则(SRM)，以统计学习理论作为理论基础，运算速度快，泛化能力强，预测精度高。
- This algorithm based on the principle of structural risk minimization can solve the problem of overfitting effectively and has good generality capability and better classification accuracy. 它基于结构风险最小化原则，能有效地解决过学习问题，具有良好的推广性和分类精确性。
- A method of outlier detection in re-gression is proposed making use of the character of structure risk function and KT condition in support vector regres-sion in this paper. 该文利用支持向量回归算法中结构风险函数的性质以及KT条件,提出一种回归中的异常值检测方法。
- There is a structural alteration to the building. 这座建筑物在这有一处结构上的改变。
- The model can minimize the regularized structure risk summation of all outputs, and it can select different kernel functions and model parameters for different outputs. 在一个优化问题中，该模型能最小化所有输出带正则项的结构风险总和，并能为不同输出选择不同的核函数及模型参数。
