In this study, we address the problem of high-dimensional binary classification. Our proposed solution involves employing an aggregation technique founded on exponential weights and empirical hinge loss. Through the employment of a suitable sparsity-inducing prior distribution, we demonstrate that our method yields favorable theoretical results on prediction error. The efficiency of our procedure is achieved through the utilization of Langevin Monte Carlo, a gradient-based sampling approach. To illustrate the effectiveness of our approach, we conduct comparisons with the logistic Lasso on simulated data and a real dataset. Our method frequently demonstrates superior performance compared to the logistic Lasso.

翻译：在本研究中，我们探讨了高维二元分类问题。所提出的解决方案采用了一种基于指数权重和经验铰链损失的聚合技术。通过引入合适的稀疏诱导先验分布，我们证明了该方法在预测误差方面具有优越的理论性能。通过采用基于梯度的抽样方法——朗之万蒙特卡洛，我们实现了该过程的高效性。为验证方法的有效性，我们将其与逻辑Lasso在模拟数据集和真实数据集上进行了比较。实验表明，我们的方法在多数情况下性能优于逻辑Lasso。