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- Risk Minimization Program 风险最小化计划
- 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。
- 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。
- 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。
- The BF status evidential samples are limited and the approach based on empirical risk minimization principle doesn't work well. 现实中高炉炉况的征兆样本集是有限的,常规的基于经验风险最小化原则的方法的应用效果并不理想。
- 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。
- Weighted Partial Least-Squared (WPLS) method was proposed to achieve Structure Risk Minimization (SRM) in the Partial Least-Squares (PLS) modeling process. 摘要为了在偏最小二乘法(PLS)建模过程中实现结构风险最小化(SRM)，提出基于结构风险最小化的加权偏最小二乘法(WPLS)。
- 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。
- Hazardous Waste Minimization Program 减少有害废物计划(海军)
- Support vector machine (SVM) is a novel learning technique based on the principles of structure risk minimization and very good application potentialities has been shown in classification and regression. 支持向量机是一种基于结构风险最小化原理的新一代机器学习方法,在分类和回归估计方面已显示出了很好的应用前景。
- 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。
- It will increase the number of risk-management experts responsible for reviewing proposed and implemented Risk Minimization Action Plans or Risk Evaluation and Mitigation Strategies (REMS). 方案将会增加风险管理专家的数量，负责对预期的风险进行回顾分析，并负责执行风险降低行动计划或者风险评价和减轻措施（REMS）。
- 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。
- Support Vector Machines is an important theory designed based on the Vapnik-Chervonenkis Dimension in statistical learning theory (SLT) and empirical risk minimization (ERM) principles. 支持向量机是建立在统计学习理论的VC维（Vapnik-Chervonenkis Dimension）理论和结构风险最小化原则基础上的重要理论。
- 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。
- After introducing machine learning problems based on data, the paper has discussed the basic problems of machine learning, empirical risk minimization (ERM), complexity and generalization capacity. 摘要通过对基于数据的机器学习问题介绍，阐述了机器学习的主要问题、经验风险最小化、复杂性与推广能力问题。
- 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。
- Tool kits of key recognizer components will be provided, so that minimal programming skills are necessary. 核心的识别组分将采用现成的工具箱，因此只需很少的编程技巧即可。
