Omega: on-line memory-based general purpose system classifier
Omega: on-line memory-based general purpose system classifier
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
ConceptNet — A Practical Commonsense Reasoning Tool-Kit
BT Technology Journal
Active Learning with Feedback on Features and Instances
The Journal of Machine Learning Research
An interactive algorithm for asking and incorporating feature feedback into support vector machines
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Learning from labeled features using generalized expectation criteria
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Fixing the program my computer learned: barriers for end users, challenges for the machine
Proceedings of the 14th international conference on Intelligent user interfaces
Uncertainty sampling and transductive experimental design for active dual supervision
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Interacting meaningfully with machine learning systems: Three experiments
International Journal of Human-Computer Studies
Interactive feature space construction using semantic information
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
AnalogySpace: reducing the dimensionality of common sense knowledge
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Active learning with statistical models
Journal of Artificial Intelligence Research
A unified approach to active dual supervision for labeling features and examples
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Explanatory Debugging: Supporting End-User Debugging of Machine-Learned Programs
VLHCC '10 Proceedings of the 2010 IEEE Symposium on Visual Languages and Human-Centric Computing
Where are my intelligent assistant's mistakes? a systematic testing approach
IS-EUD'11 Proceedings of the Third international conference on End-user development
Why-oriented end-user debugging of naive Bayes text classification
ACM Transactions on Interactive Intelligent Systems (TiiS)
d.tour: style-based exploration of design example galleries
Proceedings of the 24th annual ACM symposium 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
End-user interactions with intelligent and autonomous systems
CHI '12 Extended Abstracts on Human Factors in Computing Systems
Continuous user feedback learning for data capture from business documents
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
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When intelligent interfaces, such as intelligent desktop assistants, email classifiers, and recommender systems, customize themselves to a particular end user, such customizations can decrease productivity and increase frustration due to inaccurate predictions - especially in early stages, when training data is limited. The end user can improve the learning algorithm by tediously labeling a substantial amount of additional training data, but this takes time and is too ad hoc to target a particular area of inaccuracy. To solve this problem, we propose a new learning algorithm based on locally weighted regression for feature labeling by end users, enabling them to point out which features are important for a class, rather than provide new training instances. In our user study, the first allowing ordinary end users to freely choose features to label directly from text documents, our algorithm was both more effective than others at leveraging end users' feature labels to improve the learning algorithm, and more robust to real users' noisy feature labels. These results strongly suggest that allowing users to freely choose features to label is a promising method for allowing end users to improve learning algorithms effectively.