ABSTRACT
No abstract available.
- Crick, C., and Scassellati, B. 2009. Intention-based robot control in social games. In Proceedings of the Cognitive Society Annual Meeting, 2009.Google Scholar
- Koop, D., Scheidegger, C., Callahan, S., Freire, J., and Silva, C. 2008. Viscomplete: Automating suggestions for visualization pipelines. IEEE Transactions on Visualization and Computer Graphics 14, 6, 1691--1698. Google ScholarDigital Library
- Scheidegger, C., Vo, H., Koop, D., Freire, J., and Silva, C. 2007. Querying and creating visualizations by analogy. Visualization and Computer Graphics, IEEE Transactions on 13, 6 (Nov.-Dec.), 1560--1567. Google ScholarDigital Library
- Webb, G. I., Pazzani, M. J., and Billsus, D. 2001. Machine learning for user modeling. User Modeling and User-Adapted Interaction 11, 1-2, 19--29. Google ScholarDigital Library
Recommendations
Toward harnessing user feedback for machine learning
IUI '07: Proceedings of the 12th international conference on Intelligent user interfacesThere has been little research into how end users might be able to communicate advice to machine learning systems. If this resource--the users themselves--could somehow work hand-in-hand with machine learning systems, the accuracy of learning systems ...
Exploring individual user satisfaction within user-led development
User-led development is gaining popularity with organizations wishing to increase user involvement and control. Typically in this approach a small group of users is given the responsibility for managing the project and representing the user community in ...
Effective end-user interaction with machine learning
AAAI'11: Proceedings of the Twenty-Fifth AAAI Conference on Artificial IntelligenceEnd-user interactive machine learning is a promising tool for enhancing human productivity and capabilities with large unstructured data sets. Recent work has shown that we can create end-user interactive machine learning systems for specific ...
Comments