在温带和热带地区会出现更大的结构风险。
A much greater structural risk occurs in temperate and tropical areas.
是一种基于结构风险最小化原理的分类技术。
The SVM (support vector machines) is a classification technique based on the structural risk minimization principle.
这一修改是为解决床垫支撑硬件和相关结构风险的问题。
This change is needed to address mattress support hardware and related structural integrity hazards.
它基于结构风险最小化准则，目的是最小化泛化误差上界。
It operates on a principle, called structural risk minimization, which aims to minimize the upper bound on the expected generalization error.
这与减少RUP中描述的详细描述阶段中的体系结构风险类似。
This is similar to mitigating architectural risks in the Elaboration phase, as described in RUP.
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。
Secondly, on the basis of these bounds, the idea of the structural risk minimization principle based on birandom samples is presented.
它使用结构风险最小化原则，运用核技巧，较好地解决了学习问题。
They USES Structural Risk Minimization and the kernel trick to solve the learning problems.
最后就优化证券市场结构和防范证券市场结构风险提出了具体措施。
The thesis proposes the concrete measures on structure optimization and risk precaution of the security market at last.
支持向量机基于结构风险最小化原则，解决了小样本数据分类和泛化问题。
SVM can solve small sample problems and has good generalization ability using the principles of structural risk minimization.
不同的是，前者是基于结构风险最小化原理，后者基于经验风险最小化原理。
The difference between them is that the former is based on the structural risk minimization principle and the latter is based on the experiential risk minimization principle.
结构风险最小化归纳原则通过控制经验风险和置信范围来控制实际风险的界。
Structural risk minimization induce principle is used to control the bound on the value of achieved risk by controlling experiential risk and belief bound at the same time.
与统计分析和神经网络相比，基于结构风险最小的支持向量机有更好的分类性能。
Compared with multivariate statistics and artificial neural networks, support vector machine based on structure risk minimization has better classification performance.
基于统计学习理论中结构风险最小化原则的支持向量机是易于小样本的机器学习方法。
Support vector machine (SVM) based on the structural risk minimization of statistical learning theory is a method of machine learning for small sample set.
支持向量机(SVM)是一种基于结构风险最小化原理，具有很好推广性能的学习算法。
The support vector machine (SVM) is an algorithm based on structure risk minimizing principle and high generalization ability.
是指以企业负债除以股东权益所得的比率。它是衡量公司财务结构风险程度的一项重要指标。
A measure of a pany's leverage, calculated by dividing long-term debt by mon shareholders' equity, usually using the data from the previous fiscal year.
它基于结构风险最小化原理，能有效地解决过学习问题，具有良好的推广性和较好的分类精确性。
It based on structural risk minimization can effectively solve the over study problem and has the good extension and the better classified accuracy.
它基于结构风险最小化原理，能有效地解决过学习问题，具有良好的推广性和较好的分类精确性。
It based on structural risk minimization can effectively solve the over study problem and the good extension and better classified accuracy.
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 is a learning technique based on the structural risk minimization principle as well as a new regression method with good generalization ability.
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。
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 regression in this paper.
由于使用结构风险最小化原则代替经验风险最小化原则，使它能较好地处理小样本情况下的学习问题。
The main advantage of SVM is that it can serve better in the processing of small-sample learning problems by the replacement of Experiential Risk Minimization by Structural Risk Minimization.
支持向量机方法基于小学习样本条件下，通过寻求结构风险最小，以期获得良好的分类效果和泛化能力。
The SVM method is based on seeking on the Structural Risk Minimization by few learning samples supporting, and it has important feature such as good generalization and classification performance, etc.
本文研究成果对船舶设计和营运部门，做出船舶主体结构风险评估和制定救援计划具有一定的参考价值。
The results of this paper can supply the departments of the transport ship with the significant values, which make the captain to assess the risk for the ship hull and work out a plan for rescue.
利用支持向量回归算法中结构风险函数较好的平滑性以及KKT条件，提出一种回归中的异常值检测方法。
A method of outlier detection in regression is proposed making use of the character of structure risk function and KKT condition in support vector regression.
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。
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.
支持向量机是一种基于结构风险最小原则的新型机器学习方法，具有完备的理论依据和良好的学习泛化能力。
Support vector machines is a new statistical learning method based on structural risk minimization principle, and it has integrated theory and valid learning generalization ability.
同时，由于该方法建立在结构风险最小化准则上，而不是仅仅使经验风险最小，所以，它具有好的推广能力。
The learning discipline of SVM is to minimize the structural risk instead of empirical risk, hence the better extensibility is guaranteed.
保险风险主要有五大类：偿付能力风险、资金管理风险、经营不规范风险、经营道德风险和公司治理结构风险。
The insurance risk mainly has the five major types: Liquidate ability risk, the funds risk, the management risk, the morals risk and the company's structure risk.
该算法能针对在样本有限的情况下，采用结构风险最小化准则，把学习问题转化为一个二次规划问题来获得最优解。
The method can transfer the learning problem into a second planning to acquire the optimal solution according to the principle of structure risk minimum under limited samples situation.
支撑矢量机（SVM）是在VC理论的基础上根据结构风险最小归纳原理建立的一种比神经网络更强有力的学习机。
Support Vector Machine (SVM) is a kind of learning machines constructed according to SRM principle on the basis of VC theory, which is much more powerful man the neural networks.
支撑矢量机（SVM）是在VC理论的基础上根据结构风险最小归纳原理建立的一种比神经网络更强有力的学习机。
Support Vector Machine (SVM) is a kind of learning machines constructed according to SRM principle on the basis of VC theory, which is much more powerful man the neural networks.
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