Overview based example selection in end user interactive concept learning
Overview based example selection in end user interactive concept learning
复制标题
最终用户交互式概念学习中基于概述的示例选择
DOI：
10.1145/1622176.1622222
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发表时间：
2009
期刊：
影响因子：
3.8
通讯作者：
Desney S. Tan
中科院分区：
文献类型：
--
作者：
Saleema Amershi;J. Fogarty;Ashish Kapoor;Desney S. Tan
与大型非结构化数据集的交互是困难的，因为现有的方法，如关键字搜索，并不总是适合于描述与人们想要在数据集中进行的区分相对应的概念。一种可能的解决方案是允许最终用户训练机器学习系统来识别所需的概念，这种策略称为交互式概念学习。一个根本的挑战是设计一个既能保持最终用户灵活性和控制力，又能引导他们提供示例的系统，使机器学习系统能够有效地学习所需的概念。本文介绍了我们的设计和评估的四个新的概述为基础的方法来指导例子的选择。我们在CueFlik中进行了探索，CueFlik是一个研究Web图像搜索中最终用户交互式概念学习的系统。我们的评估表明，我们的方法不仅可以指导最终用户选择比之前性能最好的设计更好的训练示例，还可以减少不知道何时停止训练系统的影响。我们讨论了最终用户交互式概念学习系统的挑战，并确定了未来的研究机会，有效的设计这样的系统。
Interaction with large unstructured datasets is difficult because existing approaches, such as keyword search, are not always suited to describing concepts corresponding to the distinctions people want to make within datasets. One possible solution is to allow end users to train machine learning systems to identify desired concepts, a strategy known as interactive concept learning. A fundamental challenge is to design systems that preserve end user flexibility and control while also guiding them to provide examples that allow the machine learning system to effectively learn the desired concept. This paper presents our design and evaluation of four new overview based approaches to guiding example selection. We situate our explorations within CueFlik, a system examining end user interactive concept learning in Web image search. Our evaluation shows our approaches not only guide end users to select better training examples than the best performing previous design for this application, but also reduce the impact of not knowing when to stop training the system. We discuss challenges for end user interactive concept learning systems and identify opportunities for future research on the effective design of such systems.