论文标题
对约束机器学习的正规化方法的分析
An Analysis of Regularized Approaches for Constrained Machine Learning
论文作者
论文摘要
引入了基于正则化的机器学习限制方法（ML），以通过专家知识来改善预测模型。我们解决了在损失（学习者的准确性）和正规化项（约束满意度的程度）之间找到适当平衡的问题。本文的关键结果是正式的证明，这种方法不能保证找到所有最佳解决方案。特别是，在非凸vex情况下，对于受约束的问题可能会有最佳功能，与任何乘数值不符。
Regularization-based approaches for injecting constraints in Machine Learning (ML) were introduced to improve a predictive model via expert knowledge. We tackle the issue of finding the right balance between the loss (the accuracy of the learner) and the regularization term (the degree of constraint satisfaction). The key results of this paper is the formal demonstration that this type of approach cannot guarantee to find all optimal solutions. In particular, in the non-convex case there might be optima for the constrained problem that do not correspond to any multiplier value.
