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。
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。
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。
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 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。
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。
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