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摘要翻译：
本文讨论了在各种应用中，当可用的标记样本的分布与测试数据的分布有些不同时，出现的领域自适应问题。在Ben-David等人以前工作的基础上。(2007)，我们引入了一种新的分布间距离&差异距离，它适用于具有任意损失函数的自适应问题。给出了估计不同损失函数的有限样本间差异距离的Rademacher复杂度界。利用该距离，我们得到了一系列损失函数的域自适应的新的推广界。我们还提出了一系列新的适应范围，用于大类基于正则化的算法，包括基于经验差异的支持向量机和核岭回归。这激发了我们对各种损失函数的经验差异最小化问题的分析，并给出了新的算法。我们报告了初步实验的结果，证明了我们的差异最小化算法对领域自适应的好处。
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英文标题：
《Domain Adaptation: Learning Bounds and Algorithms》
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作者：
Yishay Mansour, Mehryar Mohri, Afshin Rostamizadeh
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最新提交年份：
2009
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Machine Learning 机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
This paper addresses the general problem of domain adaptation which arises in a variety of applications where the distribution of the labeled sample available somewhat differs from that of the test data. Building on previous work by Ben-David et al. (2007), we introduce a novel distance between distributions, discrepancy distance, that is tailored to adaptation problems with arbitrary loss functions. We give Rademacher complexity bounds for estimating the discrepancy distance from finite samples for different loss functions. Using this distance, we derive novel generalization bounds for domain adaptation for a wide family of loss functions. We also present a series of novel adaptation bounds for large classes of regularization-based algorithms, including support vector machines and kernel ridge regression based on the empirical discrepancy. This motivates our analysis of the problem of minimizing the empirical discrepancy for various loss functions for which we also give novel algorithms. We report the results of preliminary experiments that demonstrate the benefits of our discrepancy minimization algorithms for domain adaptation.
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PDF链接：
https://arxiv.org/pdf/0902.3430
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