论文标题
二进制分类的随机梯度下降的终止标准
A termination criterion for stochastic gradient descent for binary classification
论文作者
论文摘要
我们提出了一个新的，简单且计算廉价的终止测试，用于用于使用均匀的线性预测指标的二进制分类和铰链损失的二进制分类和铰链损耗的恒定尺寸随机梯度下降（SGD）。当数据是高斯分布时，我们的理论结果支持我们停止标准的有效性。噪声的存在允许可能存在不可分割的数据。我们表明，我们的测试在有限的迭代中终止，并且当数据中的噪声不大时，终止的预期分类器几乎可以最大程度地减少错误分类的可能性。最后，数值实验表明真实和合成数据集都表明我们的终止测试对准确性和运行时间具有良好的可预测性。
We propose a new, simple, and computationally inexpensive termination test for constant step-size stochastic gradient descent (SGD) applied to binary classification on the logistic and hinge loss with homogeneous linear predictors. Our theoretical results support the effectiveness of our stopping criterion when the data is Gaussian distributed. This presence of noise allows for the possibility of non-separable data. We show that our test terminates in a finite number of iterations and when the noise in the data is not too large, the expected classifier at termination nearly minimizes the probability of misclassification. Finally, numerical experiments indicate for both real and synthetic data sets that our termination test exhibits a good degree of predictability on accuracy and running time.
