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UIST2.0 Archive - 20 years of UIST
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learning

active learning

In Proceedings of UIST 2005
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Preference elicitation for interface optimization (p. 173-182)

end-user interactive concept learning

In Proceedings of UIST 2009
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Overview based example selection in end user interactive concept learning (p. 247-256)

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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.

inductive learning

learning

In Proceedings of UIST 2007
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Specifying label layout style by example (p. 221-230)

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Creating high-quality label layouts in a particular visual style is a time-consuming process. Although automated labeling algorithms can aid the layout process, expert design knowledge is required to tune these algorithms so that they produce layouts which meet the designer's expectations. We propose a system which can learn a labellayout style from a single example layout and then apply this style to new labeling problems. Because designers find it much easier to create example layouts than tune algorithmic parameters, our system provides a more natural workflow for graphic designers. We demonstrate that our system is capable of learning a variety of label layout styles from examples.

machine learning

In Proceedings of UIST 2007
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Eyepatch: prototyping camera-based interaction through examples (p. 33-42)

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Cameras are a useful source of input for many interactive applications, but computer vision programming is difficult and requires specialized knowledge that is out of reach for many HCI practitioners. In an effort to learn what makes a useful computer vision design tool, we created Eyepatch, a tool for designing camera-based interactions, and evaluated the Eyepatch prototype through deployment to students in an HCI course. This paper describes the lessons we learned about making computer vision more accessible, while retaining enough power and flexibility to be useful in a wide variety of interaction scenarios.

In Proceedings of UIST 2007
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Robust, low-cost, non-intrusive sensing and recognition of seated postures (p. 149-158)

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In this paper, we present a methodology for recognizing seatedpostures using data from pressure sensors installed on a chair.Information about seated postures could be used to help avoidadverse effects of sitting for long periods of time or to predictseated activities for a human-computer interface. Our system designdisplays accurate near-real-time classification performance on datafrom subjects on which the posture recognition system was nottrained by using a set of carefully designed, subject-invariantsignal features. By using a near-optimal sensor placement strategy,we keep the number of required sensors low thereby reducing costand computational complexity. We evaluated the performance of ourtechnology using a series of empirical methods including (1)cross-validation (classification accuracy of 87% for ten posturesusing data from 31 sensors), and (2) a physical deployment of oursystem (78% classification accuracy using data from 19sensors).

In Proceedings of UIST 2010
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Gestalt: integrated support for implementation and analysis in machine learning (p. 37-46)

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We present Gestalt, a development environment designed to support the process of applying machine learning. While traditional programming environments focus on source code, we explicitly support both code and data. Gestalt allows developers to implement a classification pipeline, analyze data as it moves through that pipeline, and easily transition between implementation and analysis. An experiment shows this significantly improves the ability of developers to find and fix bugs in machine learning systems. Our discussion of Gestalt and our experimental observations provide new insight into general-purpose support for the machine learning process.

unsupervised learning