对于已知行为，采用加权支持向量机分类算法来识别其行为类别；
For the known activities, the weighted support vector machine (WSVM) is used to recognize their types.
对结果均方差的分析显示，加权支持向量机的预测精度优于人工神经网络和标准支持向量机模型。
The analysis to the mean square deviation showed us the conclusion, that the prediction accuracy of WSVM was better than the ANN and traditional SVM models.
其中根据各样本重要性的不同，引入了加权支持向量机方法，然后利用免疫规划算法对其进行参数优化。
The immune programming algorithm, inspired by the immune system of human and other mammals, was used to optimize the parameters of weighted support vector machines.
基于可靠性预测这一特性，本文采用支持向量机的一种新模型——加权支持向量机对系统可靠性进行建模和预测。
Based on the very characteristic of reliability prediction, the papers used a new SVM model called Weighted SVM (WSVM) to model and predict the system reliability.
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。
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.
为了解决这个问题，本文提出了一种基于特征加权的支持向量回归机。
In order to solve the problem, support vector machine based on weighted feature is proposed in this paper.
该方法首先通过在加权最小二乘 支持向量机的基础上加入对数据偏斜的处理，解决了元 信息分类时关键词特征稀疏和样本高度不均衡问题；
Since the feature of the meta-information classification keywords is sparse and the distributing of sample is unbalanced, this thesis considered the factor of data skew based on LS-VSM.
该方法首先通过在加权最小二乘 支持向量机的基础上加入对数据偏斜的处理，解决了元 信息分类时关键词特征稀疏和样本高度不均衡问题；
Since the feature of the meta-information classification keywords is sparse and the distributing of sample is unbalanced, this thesis considered the factor of data skew based on LS-VSM.
应用推荐