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Justin Talbot

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Publications by Justin Talbot (bibliography)

» 2009 «

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Talbot, Justin, Lee, Bongshin, Kapoor, Ashish and Tan, Desney S. (2009): EnsembleMatrix: interactive visualization to support machine learning with multiple classifiers. In: Proceedings of ACM CHI 2009 Conference on Human Factors in Computing Systems 2009. pp. 1283-1292. Available online

“Machine learning is an increasingly used computational tool within human-computer interaction research. While most researchers currently utilize an iterative approach to refining classifier models and performance, we propose that ensemble classification techniques may be a viable and even preferable alternative. In ensemble learning, algorithms combine multiple classifiers to build one that is superior to its components. In this paper, we present EnsembleMatrix, an interactive visualization system that presents a graphical view of confusion matrices to help users understand relative merits of various classifiers. EnsembleMatrix allows users to directly interact with the visualizations in order to explore and build combination models. We evaluate the efficacy of the system and the approach in a user study. Results show that users are able to quickly combine multiple classifiers operating on multiple feature sets to produce an ensemble classifier with accuracy that approaches best-reported performance classifying images in the CalTech-101 dataset.”

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» 2008 «

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Alonso, Omar and Talbot, Justin (2008): Structuring collections with Scatter/Gather extensions. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2008. pp. 697-698. Available online

“A major component of sense-making is organizing -- grouping, labeling, and summarizing -- the data at hand in order to form a useful mental model, a necessary precursor to identifying missing information and to reasoning about the data. Previous work has shown the Scatter/Gather model to be useful in exploratory activities that occur when users encounter unknown document collections. However, the topic structure communicated by Scatter/Gather is closely tied to the behavior of the underlying clustering algorithm; this structure may not reflect the mental model most applicable to the information need. In this paper we describe the initial design of a mixed-initiative information structuring tool that leverages aspects of the well-studied Scatter/Gather model but permits the user to impose their own desired structure when necessary.”

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