Scatter/Gather: a cluster-based approach to browsing large document collections
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Learning with unreliable boundary queries
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
Learning from a consistently ignorant teacher
Journal of Computer and System Sciences
Principles of mixed-initiative user interfaces
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
Side views: persistent, on-demand previews for open-ended tasks
Proceedings of the 15th annual ACM symposium on User interface software and technology
Proceedings of the 8th international conference on Intelligent user interfaces
a CAPpella: programming by demonstration of context-aware applications
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
CueTIP: a mixed-initiative interface for correcting handwriting errors
UIST '06 Proceedings of the 19th annual ACM symposium on User interface software and technology
Supporting interface customization using a mixed-initiative approach
Proceedings of the 12th international conference on Intelligent user interfaces
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
CueFlik: interactive concept learning in image search
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The Journal of Machine Learning Research
Learning to generalize for complex selection tasks
Proceedings of the 14th international conference on Intelligent user interfaces
Content-Based Hierarchical Classification of Vacation Images
ICMCS '99 Proceedings of the IEEE International Conference on Multimedia Computing and Systems - Volume 2
Amplifying community content creation with mixed initiative information extraction
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
Active learning with near misses
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Active learning with multiple views
Journal of Artificial Intelligence Research
Examining multiple potential models in end-user interactive concept learning
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Human model evaluation in interactive supervised learning
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Designing for effective end-user interaction with machine learning
Proceedings of the 24th annual ACM symposium adjunct on User interface software and technology
Proceedings of the 2012 ACM international conference on Intelligent User Interfaces
Regroup: interactive machine learning for on-demand group creation in social networks
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Minimizing user effort in transforming data by example
Proceedings of the 19th international conference on Intelligent User Interfaces
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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.