COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
Solving the multiple instance problem with axis-parallel rectangles
Artificial Intelligence
A framework for multiple-instance learning
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Machine Learning
Machine Learning
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Content-Based Image Retrieval Using Multiple-Instance Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
AI '01 Proceedings of the 14th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
Solving the Multiple-Instance Problem: A Lazy Learning Approach
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
Selective Sampling for Nearest Neighbor Classifiers
Machine Learning
Active Sampling for Class Probability Estimation and Ranking
Machine Learning
Online Choice of Active Learning Algorithms
The Journal of Machine Learning Research
Supervised versus multiple instance learning: an empirical comparison
ICML '05 Proceedings of the 22nd international conference on Machine learning
Active learning with direct query construction
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Active learning with statistical models
Journal of Artificial Intelligence Research
Active cost-sensitive learning
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Active Learning with Generalized Queries
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Asking Generalized Queries to Domain Experts to Improve Learning
IEEE Transactions on Knowledge and Data Engineering
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Asking generalized queries (by regarding some features as don't-care) in active learning has been proposed and studied recently. As each generalized query is equivalent to a set of specific ones, the answers from the oracle can usually provide more information thus speeding up the learning effectively. However, as the answers to the generalized queries might be uncertain, previous studies often assume that the oracle is capable of providing (accurate) probabilistic answers. This assumption, however, is often too stringent in real-world situations. In this paper, we make a more realistic assumption that the oracle can only provide (non-probabilistic) ambiguous answers, similar to the setting in multiple-instance learning. That is, the generalized query is labeled positive if at least one of the corresponding specific queries is positive, and is labeled negative otherwise. We therefore propose an algorithm to construct the generalized queries and improve the learning model with such ambiguous answers in active learning. Empirical study shows that, the proposed algorithm can significantly speed up the learning process, and outperform active learning with either specific queries or inaccurately answered generalized queries.