图 书 馆
有效学习内核Active Kernel Learning |
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| 课程网址: | http://videolectures.net/icml08_jin_acl/ |
| 主讲教师: | Rong Jin |
| 开课单位: | 鲁汶大学 |
| 开课时间: | 2008-07-28 |
| 课程语种: | 英语 |
| 中文简介: |  识别给定数据集的适当内核功能/矩阵对于所有基于内核的学习技术都是必不可少的。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辅助信息中学习内核函数或矩阵。但是，大多数以前的研究仅限于“被动”内核学习，在这种学习中，会事先提供辅助信息。在本文中，我们提出了一个“主动内核学习”（AKL）框架，该框架能够主动识别内核学习中最有用的成对约束。主动内核学习的关键挑战是在给定示例标签类别未知的情况下如何衡量每个示例对的信息量。为此，我们提出了一种用于主动内核学习的最小最大方法，该方法选择示例对，即使对所选对的类分配不正确，也将导致最大的分类余量。我们进一步将相关的优化问题近似为凸规划问题。通过与主动内核学习的其他两种实现方式进行比较，我们评估了所提出的主动内核学习算法的有效性。对9个数据集进行数据聚类的实证研究表明，该算法比其竞争者有效得多。 |
| 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. |
| 关 键 词: | 内核功能; 内核函数; 内核矩阵; 数据聚类 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-03-25：zhouxj |
| 最后编审: | 2020-05-25：cxin |
| 阅读次数: | 42 |