调查与实验：走向学习的准确性
Survey & Experiment: Towards the Learning Accuracy
摘要：
为了达到最佳的学习精度，人们在困难和挫折中前进。虽然可以使用给定的样本集优化经验目标，但其对整个样本分布的泛化能力仍然值得怀疑。即使提供了公平的泛化保证，人们仍然想知道如果正则化子被移除会发生什么，和/或人工损失（如铰链损失）与准确性的关系如何。出于这个原因，本报告调查了四个不同的试验对学习的准确性，拥抱监督学习理论在过去四年的主要进展。从学习的一般设置开始，前两个试验引入了凸学习的最佳优化和推广界，第三个试验去掉了正则化器。作为一种创新性尝试，第四次试验研究了以准确率为目标时的优化问题，特别是在二分类的情况下。本报告还通过实验对最后一次试验进行了分析。
英文摘要：
To attain the best learning accuracy, people move on with difficulties and frustrations. Though one can optimize the empirical objective using a given set of samples, its generalization ability to the entire sample distribution remains questionable. Even if a fair generalization guarantee is offered, one still wants to know what is to happen if the regularizer is removed, and/or how well the artificial loss (like the hinge loss) relates to the accuracy. For such reason, this report surveys four different trials towards the learning accuracy, embracing the major advances in supervised learning theory in the past four years. Starting from the generic setting of learning, the first two trials introduce the best optimization and generalization bounds for convex learning, and the third trial gets rid of the regularizer. As an innovative attempt, the fourth trial studies the optimization when the objective is exactly the accuracy, in the special case of binary classification. This report also analyzes the last trial through experiments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
