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在替代丧失功能，分歧和分散检测
On Surrogate Loss Functions, f-Divergences and Decentralized Detection |
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| 课程网址: |
http://videolectures.net/mlss09us_jordan_slffddd/
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| 主讲教师: |
Michael I. Jordan
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| 开课单位: |
加州大学伯克利分校
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| 开课时间: |
2009-07-30
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| 课程语种: |
英语
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| 中文简介: |
1951年，大卫布莱克威尔发表了一篇开创性的论文 - 在经济学中被广泛引用 - 其中基于0-1损失的风险与称为f-分歧的一类函数之间建立了联系。后来的功能因此在信号处理和信息理论的几个领域发挥了重要作用，包括分散检测。然而，他们在这些领域的作用基本上是启发式的。我们表明，Blackwell＆acute程序的扩展为分散检测中f-分歧的使用以及实验设计的更一般问题提供了坚实的基础。我们的扩展是基于f-分歧和所谓的代理损失函数之间的联系 - 计算启发的0-1损失上限已经成为机器学习分类文献的核心。 （与XuanLong Nguyen和Martin Wainwright共同合作。）
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| 课程简介: |
In 1951, David Blackwell published a seminal paper - widely cited in economics - in which a link was established between the risk based on 0-1 loss and a class of functionals known as f-divergences. The latter functionals have since come to play an important role in several areas of signal processing and information theory, including decentralized detection. Yet their role in these fields has largely been heuristic. We show that an extension of Blackwell´s programme provides a solid foundation for the use of f-divergences in decentralized detection, as well as in more general problems of experimental design. Our extension is based on a connection between f-divergences and the class of so-called surrogate loss funcions - computationally-inspired upper bounds on 0-1 loss that have become central in the machine learning literature on classification. (Joint work with XuanLong Nguyen and Martin Wainwright.)
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| 关 键 词: |
计算学习理论; 机文献分类学习; 机器学习
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| 课程来源: |
视频讲座网
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| 最后编审: |
2020-06-29：yumf
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| 阅读次数: |
27
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