Information-based objective functions for active data selection
Neural Computation
Usability inspection methods
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
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
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
CueFlik: interactive concept learning in image search
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Importance weighted active learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Overview based example selection in end user interactive concept learning
Proceedings of the 22nd annual ACM symposium on User interface software and technology
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
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
An explanation-centric approach for personalizing intelligent agents
Proceedings of the 2012 ACM international conference on Intelligent User Interfaces
Tell me more?: the effects of mental model soundness on personalizing an intelligent agent
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
End-user interactions with intelligent and autonomous systems
CHI '12 Extended Abstracts on Human Factors in Computing Systems
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End-user interactive concept learning is a technique for interacting with large unstructured datasets, requiring insights from both human-computer interaction and machine learning. This note re-examines an assumption implicit in prior interactive machine learning research, that interaction should focus on the question "what class is this object?". We broaden interaction to include examination of multiple potential models while training a machine learning system. We evaluate this approach and find that people naturally adopt revision in the interactive machine learning process and that this improves the quality of their resulting models for difficult concepts.