Because the structural risk minimization principle makes SVM exhibit good generalization.
结构风险最小化原则使其具有良好的学习推广性。
A linear programming model for the risk minimization of portfolios in which short selling is not allowed is al...
文中同时还给出了不允许卖空情况下组合证券风险最小化的线性规划模型。
The SVM (support vector machines) is a classification technique based on the structural risk minimization principle.
是一种基于结构风险最小化原理的分类技术。
SVM can solve small sample problems and has good generalization ability using the principles of structural risk minimization.
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。
A linear programming model for the risk minimization of portfolios in which short selling is not allowed is also put forward.
文中同时还给出了不允许卖空情况下组合证券风险最小化的线性规划模型。
We discuss the problem of portfolio investment with risk minimization subject to nonnegative investment proportional coefficient.
本文讨论基于最小路径和最小割集的复杂系统可靠性的描述与计算问题。
It can solve small samples learning problems better by using structural risk minimization in place of experiential risk minimization.
由于采用了结构风险最小化原则替代经验风险最小化原则，使它能较好地解决小样本学习问题。
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。
It operates on a principle, called structural risk minimization, which aims to minimize the upper bound on the expected generalization error.
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 is a new statistical study method in which the traditional empirical risk minimization principle is replaced by structural risk minimization principle.
支持向量机是以统计学习理论为基础的，采用结构风险最小化原则代替传统经验风险最小化原则的新型统计学习方法。
Support vector machine (SVM) based on the structural risk minimization of statistical learning theory is a method of machine learning for small sample set.
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 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。
Compared with multivariate statistics and artificial neural networks, support vector machine based on structure risk minimization has better classification performance.
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。
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.
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。
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.
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。
We use the theory of local risk minimization for incomplete markets to determine hedging strategies for equity-linked life insurance contracts with stochastic interest rates.
提出利用不完全市场的局部风险最小对冲方法对冲保险者的风险。
Support vector machine (SVM) is a new general learning machine, which analyzes the consistency of learning and speed of convergence from structure risk minimization principle.
支持向量机(SVM)是一种新的通用学习机器，它从结构风险最小化的角度，分析了学习过程的一致性、收敛速度等。
An SVM-based recognition method for the safety of oil and gas pipeline was proposed due to limitation of the traditional learning methods based on empirical risk minimization.
针对基于经验风险最小化原则的传统学习方法的不足，提出了一种基于支持向量机的油气管道安全识别方法。
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。
Using the absolute deviation as a risk measurement index, a novel absolute deviation optimal purchasing portfolio model for multiple markets is built in risk minimization target.
用绝对离差度量供电公司的购电风险，建立以风险最小化为目标的多市场购电组合优化模型。
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.
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。
Based on the structural risk minimization principle, the latest data mining method, support vector machine (SVM) algorithm, in artificial intelligence field was introduced in this paper.
介绍了人工智能领域最新的基于结构风险最小化原理的数据挖掘算法——支持向量机算法。
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)。
Because neural network is based upon empirical risk minimization and asymptotic theories, it is suitable to deal with situations where the amount of samples is tremendous and even infinite.
神经网络的理论基础是最小化经验误差，这种基于传统的渐进理论的学习方法，在训练样本点无穷多时是适用的。
Based on the principle of construction risk minimization, the relations among the main variants are found out to yield a general rule which is then used to obtain the accurate optimizations.
根据结构风险最小化原则，在“数据有限”的情况下，找到各种主要变量之间的关系，从复杂系统中归纳出一般规律，进而准确得到优化结果。
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.
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。
It has long been recognized that the Structural Risk Minimization (SRM) principle based on the concept of VC-dimension provides an excellent means for complexity selection of a learning machine.
因此，对统计学习模型的复杂性给出评价与选择的准则，一直是一个核心问题。
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.
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。
Although the language of finance now involves It calculus, minimization of risk in a quantifiable manner underlies much of the modern theory.
虽然现在金融语言涉及到微积分，以风险最小化的量化方式构成现代理论。
Although the language of finance now involves It calculus, minimization of risk in a quantifiable manner underlies much of the modern theory.
虽然现在金融语言涉及到微积分，以风险最小化的量化方式构成现代理论。
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