Information retrieval: data structures and algorithms
Information retrieval: data structures and algorithms
Bounds on the mean classification error rate of multiple experts
Pattern Recognition Letters
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
MultiStage Cascading of Multiple Classifiers: One Man's Noise is Another Man's Data
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Detecting errors within a corpus using anomaly detection
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Designing interfaces for guided collection of knowledge about everyday objects from volunteers
Proceedings of the 10th international conference on Intelligent user interfaces
Proceedings of the 3rd international conference on Knowledge capture
Spam filter evaluation with imprecise ground truth
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
An analysis of knowledge collected from volunteer contributors
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Regression Learning with Multiple Noisy Oracles
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Anveshan: a framework for analysis of multiple annotators' labeling behavior
LAW IV '10 Proceedings of the Fourth Linguistic Annotation Workshop
Assessor error in stratified evaluation
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Automating image segmentation verification and validation by learning test oracles
Information and Software Technology
A study of the robustness of KNN classifiers trained using soft labels
ANNPR'06 Proceedings of the Second international conference on Artificial Neural Networks in Pattern Recognition
What you want is not what you get: predicting sharing policies for text-based content on facebook
Proceedings of the 2013 ACM workshop on Artificial intelligence and security
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Speculative execution of information gathering plans can dramatically reduce the effect of source I/O latencies on overall performance. However, the utility of speculation is closely tied to how accurately data values are predicted at runtime. Caching ...