Eager: programming repetitive tasks by demonstration
Watch what I do
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Multiple selections in smart text editing
Proceedings of the 7th international conference on Intelligent user interfaces
Proceedings of the 8th international conference on Intelligent user interfaces
Fewer clicks and less frustration: reducing the cost of reaching the right folder
Proceedings of the 11th international conference on Intelligent user interfaces
Eyepatch: prototyping camera-based interaction through examples
Proceedings of the 20th annual ACM symposium on User interface software and technology
CueFlik: interactive concept learning in image search
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Interactive information extraction with constrained conditional random fields
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
QuickSelect: history-based selection expansion
Proceedings of Graphics Interface 2009
Overview based example selection in end user interactive concept learning
Proceedings of the 22nd annual ACM symposium on User interface software and technology
Creating collections with automatic suggestions and example-based refinement
UIST '10 Proceedings of the 23nd annual ACM symposium on User interface software and technology
Designing for effective end-user interaction with machine learning
Proceedings of the 24th annual ACM symposium adjunct on User interface software and technology
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Selection tasks are common in modern computer interfaces: we are often required to select a set of files, emails, data entries, and the like. File and data browsers have sorting and block selection facilities to make these tasks easier, but for complex selections there is little to aid the user without writing complex search queries. We propose an interactive machine learning solution to this problem called "smart selection," in which the user selects and deselects items as inputs to a selection classifier which attempts at each step to correctly generalize to the user's target state. Furthermore, we take advantage of our data on how users perform selection tasks over many sessions, and use it to train a label regressor that models their generalization behavior: we call this process learning to generalize. We then combine the user's explicit labels as well the label regressor outputs in the selection classifier to predict the user's desired selections. We show that the selection classifier alone takes dramatically fewer mouse clicks than the standard file browser, and when used in conjunction with the label regressor, the predictions of the classifier are significantly more accurate with respect to the target selection state.