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多焦点学习及其在客户服务支持中的应用
Multi-focal Learning and Its Application to Customer Service Support |
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| 课程网址: |
http://videolectures.net/kdd09_ge_mfliacss/
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| 主讲教师: |
Yong Ge
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| 开课单位: |
新泽西州立大学
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| 开课时间: |
2009-09-14
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| 课程语种: |
英语
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| 中文简介: |
在本研究中, 我们将多焦点学习问题正式化, 将训练数据划分为几个不同的焦点组, 并在每个焦点组中学习预测模型。多焦点学习问题的动机是众多的真实世界学习应用。例如, 对于在客户服务中心遇到的相同类型的问题, 来自不同客户的问题描述可能会有很大的不同。有经验的客户通常会对问题进行更准确、更有重点的描述。相比之下, 经验不足的客户通常会提供更多样化的描述。在这种情况下, 培训数据中来自同一类的示例可以自然地出现在不同的焦点组中。因此, 有必要确定这些自然焦点群体, 并利用它们在不同的重点学习。关键的发展挑战是如何在培训数据中确定这些协调小组。作为一个案例研究, 我们利用多焦点学习来分析客户服务中心的问题。结果表明, 多焦点学习可以显著提高现有学习算法 (如支持向量机 (svm)) 对客户问题进行分类的学习精度。
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| 课程简介: |
In this study, we formalize a multi-focal learning problem, where training data are partitioned into several different focal groups and the prediction model will be learned within each focal group. The multi-focal learning problem is motivated by numerous real-world learning applications. For instance, for the same type of problems encountered in a customer service center, the problem descriptions from different customers can be quite different. The experienced customers usually give more precise and focused descriptions about the problem. In contrast, the inexperienced customers usually provide more diverse descriptions. In this case, the examples from the same class in the training data can be naturally in different focal groups. As a result, it is necessary to identify those natural focal groups and exploit them for learning at different focuses. The key developmental challenge is how to identify those focal groups in the training data. As a case study, we exploit multi-focal learning for profiling problems in customer service centers. The results show that multifocal learning can significantly boost the learning accuracies of existing learning algorithms, such as Support Vector Machines (SVMs), for classifying customer problems.
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| 关 键 词: |
支持向量机; 数据挖掘; 性能检测
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| 课程来源: |
视频讲座网
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| 最后编审: |
2020-06-06：毛岱琦(课程编辑志愿者)
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| 阅读次数: |
46
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