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从优化角度考察增压
Survey of Boosting from an Optimization Perspective |
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| 课程网址: |
http://videolectures.net/icml09_warmuth_vishwanathan_sbop/
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| 主讲教师: |
S.V.N. Vishwanathan, Manfred K. Warmuth
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| 开课单位: |
加州大学圣克鲁兹分校
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| 开课时间: |
2009-08-26
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| 课程语种: |
英语
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| 中文简介: |
提升已成为众所周知的集合方法。该算法在±标记的示例上维护分布，并且以商定的方式添加新的基础学习器。目标是获得一个基本学习者的小型线性组合，明确区分这些例子。我们关注Boosting的最新观点，在实例上分布的更新算法的特征在于使用相对熵作为正则化的最小化问题。最着名的增强算法是AdaBoost。当数据可分离时，此算法可大幅度地最大化硬边界。我们专注于最近的算法，当数据有噪声时，可以证明最大化软边界。我们将教授新算法，在相对熵正则化方面给出Boosting的统一和多功能视图，并展示如何基于最先进的优化技术来解决大规模问题。我们的目标是激励人们模仿最近的成功。 SVM社区用于扩展可解决的问题大小。这个目标具有挑战性，因为在Boosting中，正则化（相对熵）比用于SVM的那些（欧几里德距离平方）更复杂。然而，我们可以在不到一分钟的时间内在笔记本电脑上解决200K示例的密集问题。
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| 课程简介: |
Boosting has become a well known ensemble method. The algorithm maintains a distribution on the ±-labeled examples and a new base learner is added in a greedy fashion. The goal is to obtain a small linear combination of base learners that clearly separates the examples. We focus on a recent view of Boosting where the update algorithm for distribution on the examples is characterized by a minimization problem that uses a relative entropy as a regularization. The most well known boosting algorithms is AdaBoost. This algorithm approximately maximizes the hard margin, when the data is separable. We focus on recent algorithms that provably maximize the soft margin when the data is noisy. We will teach the new algorithms, give a uni ed and versatile view of Boosting in terms of relative entropy regularization, and show how to solve large scale problems based on state of the art optimization techniques. Our goal is to motivate people to mimic the recent successes of the SVM community for scaling up the solvable problem size. This goal is challenging because in Boosting the regularization (relative entropy) is more complicated than the one used for SVMs (squared Euclidean distance). Nevertheless we can solve dense problems with 200K examples in less than a minute on a laptop.
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| 关 键 词: |
维护分布; 基础学习器; 小型线性组合
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| 课程来源: |
视频讲座网
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| 最后编审: |
2019-04-24：cwx
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| 阅读次数: |
45
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