The key problem of training support vector machines is how to solve quadratic programming problem, but for large training examples, the problem is too difficult.
训练支持向量机的本质问题就是求解二次规划问题，但对大规模的训练样本来说，求解二次规划问题困难很大。
In order to improve the training efficiency, an advanced Fuzzy Support Vector Machine (FSVM) algorithm based on the density clustering (DBSCAN) is proposed.
为了提高模糊支持向量机在数据集上的训练效率，提出一种改进的基于密度聚类(DBSCAN)的模糊支持向量机算法。
Traditional Support Vector Machine (SVM), which based on batch training, can't satisfy the real-time requirement of environmental pollution prediction with large scale data.
传统支持向量机基于批量训练方法，无法适应环境污染预测中的海量数据与实时性要求。
Through support vector machine algorithms for gene expression data classification training, SVMs provide a effective way for analysis of gene expression data.
通过支持向量机训练算法对基因表达数据进行分类训练，为分析基因数据提供有效的手段。
The cost of the training of support vector machine is too much, if we use thousands of support vectors directly, the computation time would be too long.
支持向量机的训练代价太大，如果直接把成千上万个特征向量直接用作训练，运算时间难以忍受。
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。
Least square support vector machines regression without sparsity needs longer training time currently, and is not adapted to online real-time training.
现有最小二乘支持向量机回归的训练和模型输出的计算需要较长的时间，不适合在线实时训练。
A new least squares support vector machines based on boundary nearest was proposed, which reduced the number of support vector by using boundary nearest methods pruning the training Sam.
文中提出了一种基于边界近邻的最小二乘支持向量机，采用寻找边界近邻的方法对训练样本进行修剪，以减少了支持向量的数目。
However, the training procedure of support vector machines amounts to solving a constrained quadratic programming.
然而，支持向量机的训练过程等价于求解一个约束凸二次规划。
In the classification experiment, we find that the number of the support vector is far less than the number of the training sample number.
在分类实验中，我们发现支持向量的数量远远小于样本数，这为我们解决大规模数据问题提供了方法。
Efficient extraction of image texture features are used on the following support vector machine classifier learning and training have a very important role.
图像纹理特征的有效提取对下面所用到的支持向量机分类器来进行学习和训练有非常重要的作用。
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。
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。
Extract 21-dimensional statistical characteristics of histogram characteristic function (HCF) field, training classifier with the support vector machine (SVM).
提取直方图特征函数(HCF)域21维统计矩特征组成特征向量，用支持向量机(SVM)训练分类器。
Faced with the fact that training samples belonging to normal operation status are much more than ones belonging to abnormal operation status, the weighted support vector machine is presented.
针对污水处理过程运行状态监控中的正常运行状态样本数多而异常运行状态样本数少的特点，提出加权支持向量机方法。
An incremental training method for support vector machine is proposed to alleviate the computing burden of large-scale, high-dimension samples in multi-component gas analyzing.
针对大规模高维气体分析样本难以计算的问题，提出一种提升的支持向量机学习方法。
This paper studies the speaker identification problem using support vector machine, and presents a SVM training method on large-scale training data according to the speech signal.
本文提出了用支持向量机来解决说话人辨认问题，结合语音信号的特点，解决了大数据量情况下支持向量机的训练问题。
Through support vector machine algorithms for data classification training, SVMs provide a effective way for analysis of this data.
通过支持向量机训练算法对数据进行分类训练，为分析数据提供有效的手段。
The support vector machine is a learning algorithm, which has a good classification ability for limited training samples.
支撑矢量机是一种能在训练样本数很少的情况下达到很好分类推广能力的学习算法。
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。
The generalization of the support vector regression model, the optimization of the generalization capacity, and the training speed are discussed.
同时对广泛的支持向量回归模型、优化支持向量模型的泛化能力和运算速度等方面进行讨论。
Adopts a classifier support vector machine which is a very good training algorithm, which can acquire very good generalization when the training datum are very few.
文中采用的分类器——支持向量机是一种能在训练样本数很少的情况下达到很好分类推广能力的学习算法。
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。
This approach exploits unlabeled data for efficient clustering, which is applied in the classification with support vector machine (SVM) in the case of small-size training samples.
该方法利用大量的未标识数据进行有效聚类，并将聚类结果用于小样本情形下的支持向量机分类。
Support vector machine (SVM) is an effective method for resolving regression problem, however, traditional SVM is very sensitive to noises and outliers in the training sample.
支持向量机(SVM)是解决回归问题的一种有效的方法，但传统的支持向量机对样本中的噪声和孤立点非常敏感。
Support vector machine (SVM) is an effective method for resolving regression problem, however, traditional SVM is very sensitive to noises and outliers in the training sample.
支持向量机(SVM)是解决回归问题的一种有效的方法，但传统的支持向量机对样本中的噪声和孤立点非常敏感。
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