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主动核学习
Active Kernel Learning |
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| 课程网址: |
http://videolectures.net/icml08_jin_acl/
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| 主讲教师: |
Rong Jin
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| 开课单位: |
天主教鲁汶大学
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| 开课时间: |
2008-07-28
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| 课程语种: |
英语
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| 中文简介: |
识别给定数据集的适当内核函数/矩阵对于所有基于内核的学习技术都是必不可少的。在过去，已经提出了许多内核学习算法，以标记示例或成对约束的形式从边信息学习内核函数或矩阵。然而，大多数先前的研究仅限于“被动”内核学习，其中预先提供辅助信息。在本文中，我们提出了一个“主动内核学习”（AKL）框架，它能够主动识别内核学习中最具信息性的成对约束。主动内核学习的关键挑战是如何测量每个示例对的信息性，因为它的类标签是未知的。为此，我们提出了一种用于主动内核学习的最小最大方法，该方法选择将导致最大分类余量的示例对，即使对所选对的类分配不正确也是如此。我们进一步将相关的优化问题逼近为凸规划问题。我们通过将其与活动内核学习的其他两个实现进行比较来评估所提出的主动内核学习算法的有效性。对数据聚类的九个数据集的实证研究表明，该算法比竞争对手更有效。
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| 课程简介: |
Identifying the appropriate kernel function/matrix for a given dataset is essential to all kernel-based learning techniques. In the past, a number of kernel learning algorithms have been proposed to learn kernel functions or matrices from side information, in the form of labeled examples or pairwise constraints. However, most previous studies are limited to the "passive" kernel learning in which the side information is provided beforehand. In this paper we present a framework of "Active Kernel Learning" (AKL) that is able to actively identify the most informative pairwise constraints for kernel learning. The key challenge of active kernel learning is how to measure the informativeness of each example pair given its class label is unknown. To this end, we propose a min-max approach for active kernel learning that selects the example pairs that will lead to the largest classification margin even when the class assignments to the selected pairs are incorrect. We furthermore approximate the related optimization problem into a convex programming problem. We evaluate the effectiveness of the proposed active kernel learning algorithm by comparing it with two other implementations of active kernel learning. Empirical study with nine datasets on data clustering shows that the proposed algorithm is considerably more effective than its competitors.
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| 关 键 词: |
数据集; 内核函数; 算法
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| 课程来源: |
视频讲座网
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| 最后编审: |
2019-04-18：cwx
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| 阅读次数: |
55
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