Affective computing
Visual classification: an interactive approach to decision tree construction
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Visualizing high-dimensional predicitive model quality
Proceedings of the conference on Visualization '00
Visualizing the simple Baysian classifier
Information visualization in data mining and knowledge discovery
Gaining insights into support vector machine pattern classifiers using projection-based tour methods
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Interactive machine learning: letting users build classifiers
International Journal of Human-Computer Studies
Proceedings of the 8th international conference on Intelligent user interfaces
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Learning and reasoning about interruption
Proceedings of the 5th international conference on Multimodal interfaces
Examining task engagement in sensor-based statistical models of human interruptibility
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
One-Shot Learning of Object Categories
IEEE Transactions on Pattern Analysis and Machine Intelligence
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Using Visual Interpretation of Small Ensembles in Microarray Analysis
CBMS '06 Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems
Demonstrating the feasibility of using forearm electromyography for muscle-computer interfaces
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Experience sampling for building predictive user models: a comparative study
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Feasibility and pragmatics of classifying working memory load with an electroencephalograph
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
EnsembleMatrix: interactive visualization to support machine learning with multiple classifiers
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Examining difficulties software developers encounter in the adoption of statistical machine learning
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Human model evaluation in interactive supervised learning
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Why-oriented end-user debugging of naive Bayes text classification
ACM Transactions on Interactive Intelligent Systems (TiiS)
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
Using multiple models to understand data
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Assisted descriptor selection based on visual comparative data analysis
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
IUI workshop on interactive machine learning
Proceedings of the companion publication of the 2013 international conference on Intelligent user interfaces companion
Visualizing the performance of classification algorithms with additional re-annotated data
CHI '13 Extended Abstracts on Human Factors in Computing Systems
Customizing by doing for responsive video game characters
International Journal of Human-Computer Studies
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Interest has been growing within HCI on the use of machine learning and reasoning in applications to classify such hidden states as user intentions, based on observations. HCI researchers with these interests typically have little expertise in machine learning and often employ toolkits as relatively fixed "black boxes" for generating statistical classifiers. However, attempts to tailor the performance of classifiers to specific application requirements may require a more sophisticated understanding and custom-tailoring of methods. We present ManiMatrix, a system that provides controls and visualizations that enable system builders to refine the behavior of classification systems in an intuitive manner. With ManiMatrix, users directly refine parameters of a confusion matrix via an interactive cycle of re-classification and visualization. We present the core methods and evaluate the effectiveness of the approach in a user study. Results show that users are able to quickly and effectively modify decision boundaries of classifiers to tai-lor the behavior of classifiers to problems at hand.