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.
结构风险最小化原则使其具有良好的学习推广性。
Because the structural risk minimization principle makes SVM exhibit good generalization.
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.
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.
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.
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.
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.
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.
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。
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.
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。
It can solve small samples learning problems better by using structural risk minimization in place of experiential risk minimization.
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.
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.
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.
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。
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(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。
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.
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。
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.
尽早的关注体系结构，从而最小化风险，组织好开发。
Focus on the architecture early to minimize risks and organize development.
模仿市场领导者，寻求风险最小化与利润最大化，在组织结构中则需同时兼顾有机式与机械式的元素。
Minimizing risks and maximizing profitability by copying market leaders requires both organic and mechanistic elements in the organization's structure.
模仿市场领导者，寻求风险最小化与利润最大化，在组织结构中则需同时兼顾有机式与机械式的元素。
Minimizing risks and maximizing profitability by copying market leaders requires both organic and mechanistic elements in the organization's structure.
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