Experiments with query acquisition and use in document retrieval systems
SIGIR '90 Proceedings of the 13th annual international ACM SIGIR conference on Research and development in information retrieval
Computational learning theory: survey and selected bibliography
STOC '92 Proceedings of the twenty-fourth annual ACM symposium on Theory of computing
The Utility of Knowledge in Inductive Learning
Machine Learning
Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
Information Processing and Management: an International Journal - Special issue on interactivity at the text retrieval conference (TREC)
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Explanation-Based Generalization: A Unifying View
Machine Learning
Explanation-Based Learning: An Alternative View
Machine Learning
Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Incorporating Prior Knowledge into Boosting
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Less is More: Active Learning with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Incremental Support Vector Machine Construction
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Using terminological feedback for web search refinement: a log-based study
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
Incorporating prior knowledge with weighted margin support vector machines
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Document classification through interactive supervision of document and term labels
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Automatic Information Organization and Retrieval.
Automatic Information Organization and Retrieval.
The sentimental factor: improving review classification via human-provided information
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Budgeted learning of nailve-bayes classifiers
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
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
Selective generation of training examples in active meta-learning
International Journal of Hybrid Intelligent Systems - HIS 2007
Active Learning Strategies for Multi-Label Text Classification
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Active dual supervision: reducing the cost of annotating examples and features
HLT '09 Proceedings of the NAACL HLT 2009 Workshop on Active Learning for Natural Language Processing
Modeling annotators: a generative approach to learning from annotator rationales
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Legal docket-entry classification: where machine learning stumbles
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Interactive annotation learning with indirect feature voting
SRWS '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Student Research Workshop and Doctoral Consortium
Learning from the report-writing behavior of individuals
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Active Learning of Instance-Level Constraints for Semi-supervised Document Clustering
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Towards maximizing the accuracy of human-labeled sensor data
Proceedings of the 15th international conference on Intelligent user interfaces
On-line evolving image classifiers and their application to surface inspection
Image and Vision Computing
Interactive retrieval based on faceted feedback
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Active learning for biomedical citation screening
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
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
End-user feature labeling: a locally-weighted regression approach
Proceedings of the 16th international conference on Intelligent user interfaces
Learning to ask the right questions to help a learner learn
Proceedings of the 16th international conference on Intelligent user interfaces
Filtering semi-structured documents based on faceted feedback
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Assessing benefit from feature feedback in active learning for text classification
CoNLL '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning
Combining meta-learning and active selection of datasetoids for algorithm selection
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
Where are my intelligent assistant's mistakes? a systematic testing approach
IS-EUD'11 Proceedings of the Third international conference on End-user development
Active supervised domain adaptation
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Designing robot learners that ask good questions
HRI '12 Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction
Combining Uncertainty Sampling methods for supporting the generation of meta-examples
Information Sciences: an International Journal
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
A utility-theoretic ranking method for semi-automated text classification
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
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We extend the traditional active learning framework to include feedback on features in addition to labeling instances, and we execute a careful study of the effects of feature selection and human feedback on features in the setting of text categorization. Our experiments on a variety of categorization tasks indicate that there is significant potential in improving classifier performance by feature re-weighting, beyond that achieved via membership queries alone (traditional active learning) if we have access to an oracle that can point to the important (most predictive) features. Our experiments on human subjects indicate that human feedback on feature relevance can identify a sufficient proportion of the most relevant features (over 50% in our experiments). We find that on average, labeling a feature takes much less time than labeling a document. We devise an algorithm that interleaves labeling features and documents which significantly accelerates standard active learning in our simulation experiments. Feature feedback can complement traditional active learning in applications such as news filtering, e-mail classification, and personalization, where the human teacher can have significant knowledge on the relevance of features.