Interface Metaphors for Interactive Machine Learning

Conference · Fri Jul 14 00:00:00 EDT 2017

DOI:https://doi.org/10.1007/978-3-319-58628-1_39· OSTI ID:1373864

Jasper, Robert J.; Blaha, Leslie M.

To promote more interactive and dynamic machine learn- ing, we revisit the notion of user-interface metaphors. User-interface metaphors provide intuitive constructs for supporting user needs through interface design elements. A user-interface metaphor provides a visual or action pattern that leverages a user’s knowledge of another domain. Metaphors suggest both the visual representations that should be used in a display as well as the interactions that should be afforded to the user. We argue that user-interface metaphors can also offer a method of extracting interaction-based user feedback for use in machine learning. Metaphors offer indirect, context-based information that can be used in addition to explicit user inputs, such as user-provided labels. Implicit information from user interactions with metaphors can augment explicit user input for active learning paradigms. Or it might be leveraged in systems where explicit user inputs are more challenging to obtain. Each interaction with the metaphor provides an opportunity to gather data and learn. We argue this approach is especially important in streaming applications, where we desire machine learning systems that can adapt to dynamic, changing data.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (US)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 1373864
• Report Number(s): PNNL-SA-124439
• Resource Relation: 11th International Conference on Augmented Cognition: Augmented Cognition. Neurocognition and Machine Learning (AC 2017) July 9-14, 2017, Vancouver, BC, Canada. Lecture Notes in Computer Science, 10284:521-534
• Country of Publication: United States
• Language: English

Related Subjects

Interactive machine learning
User Interface
User Interface Metaphor