关键词: 随机梯度下降（SGD）, 权, 非均衡数据, 大规模学习, 支持向量机（SVM）

Abstract: Stochastic gradient descent (SGD) has been applied to large scale support vector machine (SVM) training.  Stochastic gradient descent takes a random way to select points during training process, this leads to a result that the probability of choosing majority class is far greater than that of choosing minority class for imbalanced classification problem. In order to deal with large scale imbalanced data classification problems, this paper proposes a method named weighted stochastic gradient descent algorithm for SVM. After the samples in the majority class are assigned a smaller weight while the samples in the minority class are assigned a larger weight, the weighted stochastic gradient descent algorithm will be used to solving the primal problem of SVM, which helps to reduce the hyperplane offset to the minority class, thus solves the large scale imbalanced data classification problems.

Key words: stochastic gradient descent (SGD), weight, imbalanced data, large scale learning, support vector machine (SVM)