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摘要翻译：
在实际应用中，经常需要机器学习算法来学习优化领域特定性能度量的分类器。以往的研究主要集中在孤立地学习所需的分类器，而对于非线性和非光滑性能度量的非线性分类器的学习仍然很困难。在本文中，我们不是直接通过优化特定性能指标来学习所需的分类器，而是提出了一种新的两步学习方法CAPO，即先用现有的学习方法训练非线性辅助分类器，然后根据特定性能指标来调整辅助分类器。在第一步中，通过采用现成的学习算法可以高效地获得辅助分类器。对于第二步，我们证明了分类器自适应问题可以归结为一个类似于线性SVMperf的二次规划问题，并且可以有效地求解。通过开发非线性辅助分类器，CAPO可以生成非线性分类器，在保持较高计算效率的前提下，优化包括基于列联表和AUC的所有性能指标在内的多种性能指标。实证研究表明，CAPO算法是有效的，计算效率高，甚至比线性SVMPERF算法更有效。
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英文标题：
《Efficient Optimization of Performance Measures by Classifier Adaptation》
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作者：
Nan Li and Ivor W. Tsang and Zhi-Hua Zhou
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最新提交年份：
2012
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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：
In practical applications, machine learning algorithms are often needed to learn classifiers that optimize domain specific performance measures. Previously, the research has focused on learning the needed classifier in isolation, yet learning nonlinear classifier for nonlinear and nonsmooth performance measures is still hard. In this paper, rather than learning the needed classifier by optimizing specific performance measure directly, we circumvent this problem by proposing a novel two-step approach called as CAPO, namely to first train nonlinear auxiliary classifiers with existing learning methods, and then to adapt auxiliary classifiers for specific performance measures. In the first step, auxiliary classifiers can be obtained efficiently by taking off-the-shelf learning algorithms. For the second step, we show that the classifier adaptation problem can be reduced to a quadratic program problem, which is similar to linear SVMperf and can be efficiently solved. By exploiting nonlinear auxiliary classifiers, CAPO can generate nonlinear classifier which optimizes a large variety of performance measures including all the performance measure based on the contingency table and AUC, whilst keeping high computational efficiency. Empirical studies show that CAPO is effective and of high computational efficiency, and even it is more efficient than linear SVMperf.
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PDF链接：
https://arxiv.org/pdf/1012.0930
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