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改善成对限制的分类︰ 一种基于利润的方法

Improving Classification with Pairwise Constraints: A Margin-based Approach
课程网址: http://videolectures.net/ecmlpkdd08_nguyen_icwpc/  
主讲教师: Rich Caruana; Nam Nguyen
开课单位: 康奈尔大学
开课时间: 2008-10-10
课程语种: 英语
中文简介:
本文讨论了一个半监督学习问题,即一对例子是属于同一类,还是属于不同类,在有少量标记数据的情况下,用成对约束进行扩充。我们引入了一种识别性学习方法,将成对约束合并到传统的基于边际的学习框架中。我们还提出了一种有效的算法PCSVM来解决成对约束学习问题。对15个数据集的实验表明,成对约束信息显著提高了分类性能。
课程简介: In this paper, we address the semi-supervised learning problem when there is a small amount of labeled data augmented with pairwise constraints indicating whether a pair of examples belongs to a same class or different classes. We introduce a discriminative learning approach that incorporates pairwise constraints into the conventional margin-based learning framework. We also present an efficient algorithm, PCSVM, to solve the pairwise constraint learning problem. Experiments with 15 data sets show that pairwise constraint information significantly increases the performance of classification.
关 键 词: 机器学习; 成对限制; 分类
课程来源: 视频讲座网
最后编审: 2021-02-03:nkq
阅读次数: 39