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超平面分类器在异构环境下的压缩编码

Compact Coding for Hyperplane Classifiers in Heterogeneous Environment
课程网址: http://videolectures.net/ecmlpkdd2011_shao_compact/  
主讲教师: Hao Shao
开课单位: 九州大学
开课时间: 2011-10-03
课程语种: 英语
中文简介:
转移学习技术在实际应用中取得了重大进展, 需要从以前的任务中获得知识, 以降低查询目标任务的标记信息的高昂成本。然而, 如何避免因异构环境中任务分布不同而发生的负转移, 仍然是一个悬而未决的问题。为了解决这类问题, 我们提出了一种在感应转移学习设置的两级框架下的超平面分类器 (cchc) 紧凑型编码方法。与传统方法不同, 我们通过最小编码从宏观层面测量任务之间的相似性。特别是, 相似度由每个源任务相对于目标任务的类边界的相关代码长度表示。此外, 源任务的信息部分在微观层面的视点中进行自适应选择, 以使特定源任务的选择更加准确。大量的实验表明了我们的算法在 uci 和文本数据集的分类精度方面的有效性。
课程简介: Transfer learning techniques have witnessed a significant development in real applications where the knowledge from previous tasks are required to reduce the high cost of inquiring the labeled information for the target task. However, how to avoid negative transfer which happens due to different distributions of tasks in heterogeneous environment is still a open problem. In order to handle this kind of issue, we propose a Compact Coding method for Hyperplane Classifiers (CCHC) under a two-level framework in inductive transfer learning setting. Unlike traditional methods, we measure the similarities among tasks from the macro level perspective through minimum encoding. Particularly speaking, the degree of the similarity is represented by the relevant code length of the class boundary of each source task with respect to the target task. In addition, informative parts of the source tasks are adaptively selected in the micro level viewpoint to make the choice of the specific source task more accurate. Extensive experiments show the effectiveness of our algorithm in terms of the classification accuracy in both UCI and text data sets.
关 键 词: 学习迁移技术; 超平面分类编码方法; 文本数据集
课程来源: 视频讲座网
最后编审: 2020-06-15:wuyq
阅读次数: 45