As an effect tool of pattern recognition and data processing, rough set theory (RST) and support vector machine (SVM) have become the focus of research in machine learning.
粗糙集理论(rst)与支持向量机(SVM)作为模式识别，数据处理的有效工具，已成为机器学习的研究热点。
By using rough set theory, this paper structures classification rules and processes the support vector machine feedback results with learning the train set.
利用粗糙集理论，通过对训练集的学习，构造分类规则，对支持向量机反馈后的结果再次进行处理。
To solve the problem that support vector machine(SVM) can only classify the small samples set, a new algorithm which applied SVM to density clustering is proposed.
为了解决支持向量机的分类仅应用于较小样本集的问题，提出了一种密度聚类与支持向量机相结合的分类算法。
Relevance feedback algorithm based on support vector machine and rough set for image retrieval is approached.
研究基于支持向量机和粗糙集的相关反馈图像检索算法。
A new predication method of customer credit of Banks is proposed based on the support vector domain classification model of non-balance data set.
基于非平衡数据集的支持向量域分类模型，提出了一种银行客户个人信用预测方法。
Support vector machine constructs an optimal hyperplane utilizing a small set of vectors near boundary.
支持向量机利用接近边界的少数向量来构造一个最优分类面。
That using the reduction attributes of rough set reduced some redundant attributes, improved the real time of data processing by support vector machine, and shorten the time for training sample.
利用粗糙集的属性约简性来约简掉一些冗余属性，提高了支持向量机进行数据处理的实时性，缩短了训练样本的时间。
Assessment method using the distance-sensitive feature selection on the feature set, and then enters the sensitive features of support vector machine classification.
然后采用距离评估方法对特征集挑选敏感特征，将敏感特征输入支持向量机进行分类。
Using Intelligent prediction system which is constituted by rough set and support vector machine, this paper studies the low efficiency of project cost prediction.
本文采用粗糙集和支持向量机这些数学方法构成智能预测系统，研究解决工程造价预测效率不高这一难题。
In addition, we combined SVM with rough set theory, and got rough support vector machine algorithm (RSVM).
此外，将支持向量机与粗糙集理论相结合，得到粗糙支持向量机算法(RSVM)。
And combining rough set and support vector machine, the mixture prediction model is established based on rough set and support vector machine.
将粗糙集与支持向量机两者结合，并建立了基于粗糙集——支持向量机的混合预测模型。
In the stage I, called as data preprocessing, the support vector machines for regression (SVMR) approach is used to filter out the outliers in the training data set.
第一阶段称为所谓的资料预先处理，即使用支援向量回归来找出训练资料集中的离异点并删除之。
To calculate the support of an item-set the authors define characteristic matrix and characteristic vector of debase.
为了计算项目集的支持度，提出了数据库特征矩阵和特征矢量的概念。
Explores the training problems of support vector machine with large training pattern set, and a new parallel algorithm based on orthogonal array is presented.
对大规模训练样本的支持向量机训练问题进行探索，提出了一种基于正交表的并行学习算法。
The overall predictive accuracy of the classification models using support vector machine were 95.9% for the fathead minnow test set and 95.0% for the honey bee test set.
其中，利用支持向量机分类算法得到的分类模型对呆鲦鱼和蜜蜂毒性测试集的整体预测准确度分别达到95.9%和95.0%。
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。
To combine the attribute reduction algorithm and the incremental training algorithm of support vector machine, a support vector machine classifier based on rough set is constructed.
将属性约简算法和支持向量机增量训练算法相结合，构造基于粗糙集数据预处理的支持向量机分类器。
This paper presents an application of least squares support vector machines in small-set pressed protuberant character recognition.
将最小二乘支持向量机引入到小字符集压印字符识别中。
According to the border region of rough set theory and the merits of V-support vector machine, the algorithm of support vector clustering is improved.
根据粗糙集理论的边界区域和V -支持向量机的优点对支持向量聚类算法进行改进。
A method of object's performance classification based on Rough Set (RS) and Support Vector Machines (SVM) was proposed and it classifies the object's performance by composing the RS and SVM.
提出了一种基于粗糙集(RS)和支持向量机(SVM)的目标对象的性能分类方法，该方法将RS和SVM结合在一起对性能进行分类。
Similarly, based on rough set theory to feature-set reduction, in the optimal decision based on the use of the property least squares support vector machine classifier to identify the flow pattern.
同样，基于粗糙集理论对特征集进行约简，在最优决策属性的基础上使用最小二乘支持向量机分类器对流型进行识别。
Rough set was applied to clustering method in view of soft kernel of support vector clustering(SVC).
支持向量聚类是基于支持向量机和核方法的一种新颖的聚类方法。
When training sets with uneven class sizes are used, the classification error based on C-Support Vector Machine is undesirably biased towards the class with fewer samples in the training set.
SVM分类算法在不同类别样本数目不均衡的情况下，训练时的分类错误倾向于样本数目小的类别。样本集中出现重复样本时作为新样本重新计算，增加了算法的训练时间。
When training sets with uneven class sizes are used, the classification error based on C-Support Vector Machine is undesirably biased towards the class with fewer samples in the training set.
SVM分类算法在不同类别样本数目不均衡的情况下，训练时的分类错误倾向于样本数目小的类别。样本集中出现重复样本时作为新样本重新计算，增加了算法的训练时间。
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